feat(applications): submit Google Sr Data Engineer + Kraken SRE AI Agents (2026-06-15)
Two applications sent and finalized on 2026-06-15: - Google - Senior Data Engineer (Merchant Data Science, Zurich), 85.5/100. Tier-1 scope fix + both Tier-2 polish applied: re-scoped the Swisscom migration claim in resume B2 + CL P2 (Scope-Discipline), added project- delivery vocab (B4), and JD-exact 'distributed data processing' (B5). - Kraken (Payward) - SRE, AI Agents (remote, CH-eligible), 87.2/100. Finalized as-is; crypto-native + production-ML edge, honest infra gaps. Logs both as 'applied' in job_scout/state/decisions.json and flips their CLAUDE.md Active Sessions rows to SENT. Open item for both: confirm level and comp clear the 180k+ all-in bar at the recruiter stage. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Site Reliability Engineer - AI Agents — Kraken (Payward)
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JD source: live scrape 2026-06-15 via Playwright (Ashby board)
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URL: https://jobs.ashbyhq.com/kraken.com/c331de1b-b75a-48f5-9d19-0e56ccb935ab
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Location: Remote — Switzerland eligible (+ UK, EU, LATAM, others)
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Employment: Full time · Remote · Engineering / SRE / DevOps
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--- VERBATIM POSTING TEXT ---
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Building the Future of Open Finance
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Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.
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Before you apply, we encourage you to explore our culture page to understand what drives us and how we work.
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The team
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Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.
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The AI Infrastructure team sits within the Data organization and is responsible for building, operating, and scaling the systems that power AI agents in production — both internal tools and external-facing products. Working closely with the AI and Agent Systems teams, this group ensures that the orchestration, execution, and model-serving layers underpinning agentic workflows are reliable, observable, and built to scale.
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This team operates at the intersection of data infrastructure and applied AI — a space that moves fast and demands engineers who can bring production discipline to emerging technology. You'll partner across Data Engineering, ML, and product-facing teams to harden agent infrastructure and keep it running at the standards our users expect.
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Importantly, this is a platform engineering team. Beyond operating infrastructure, the team is responsible for building the APIs, SDKs, and platform capabilities that enable AI, Data, and Engineering teams to safely and efficiently consume agent infrastructure as a service. Success in this role requires thinking beyond infrastructure operations and toward developer experience, platform adoption, and long-term scalability.
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The opportunity
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Design, build, and operate the infrastructure layer supporting AI agent workflows in production
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Ensure reliability, scalability, and observability of agentic systems across internal and external products
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Design and develop platform services, APIs, SDKs, and self-service capabilities that allow engineering teams to easily consume AI infrastructure and agent platform services
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Manage and maintain the compute, orchestration, and serving infrastructure powering model inference and agent execution
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Implement robust monitoring, alerting, and incident response procedures tailored to AI/ML workloads
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Utilize Infrastructure as Code (IaC) tools such as Terraform to provision and manage cloud (AWS) infrastructure components
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Build and maintain CI/CD pipelines that support rapid, reliable deployment of AI services and agent workflows
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Define and implement guardrails, failure handling, and recovery patterns specific to agentic and LLM-powered systems
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Collaborate with AI and Data Engineering teams to translate experimental agent prototypes into hardened production systems
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Manage containerized workloads using Kubernetes, ensuring efficient deployment, scaling, and orchestration of AI services
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Implement access controls and security best practices across AI infrastructure environments
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Document architecture, runbooks, and best practices to support knowledge sharing across the team
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What You Bring
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5+ years of experience as a Site Reliability Engineer, Infrastructure Engineer, Platform Engineer, or similar role in a production environment
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Hands-on experience supporting ML infrastructure, model serving, or MLOps workflows in production
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Experience building developer platforms, internal tooling, APIs, or SDKs consumed by engineering teams at scale
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Strong understanding of platform engineering principles, including developer experience, self-service infrastructure, and API-driven platform design
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Proficiency with Infrastructure as Code tools, particularly Terraform
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Experience with containerization and orchestration, particularly Kubernetes and Docker
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Solid understanding of cloud infrastructure, preferably AWS
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Strong scripting skills (bash/shell) and proficiency in at least one programming language (Python preferred)
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Experience designing and operating observability, monitoring, and alerting systems
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Experience implementing incident response procedures and participating in on-call rotations
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Strong collaboration skills working across data, AI, and engineering teams
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High ownership mindset in a fast-moving, high-stakes production environment
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Nice to haves
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Experience building or operating infrastructure for agent-based or LLM-powered systems
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Familiarity with agent orchestration frameworks (e.g., LangGraph, CrewAI, or similar)
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Background in data infrastructure, including familiarity with Airflow, Kafka, Spark, or data lake tooling
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Experience with CI/CD pipelines and deployment automation for AI/ML workloads
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Exposure to evaluation frameworks and model performance monitoring at scale
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Experience working in fast-moving 0→1 environments or platform-building teams
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Experience building SDKs, developer tooling, or internal platform products with a strong focus on usability and adoption
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Experience with Cloudflare's cloud platform and product ecosystem, including networking, security, performance, and Zero Trust solutions
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Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis.
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Note: applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance/graduation. Kraken encourages applicants to apply even if they don't fully meet the listed requirements, especially if passionate or knowledgeable about crypto.
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# Critique: Kraken (Payward) — Site Reliability Engineer, AI Agents
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**Resume File:** `output/Kraken_SRE_AI_Agents/e2e_kraken_sre_ai_agents_resume.tex`
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**CL File:** `output/Kraken_SRE_AI_Agents/e2e_kraken_sre_ai_agents_cover_letter.tex`
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**JD source:** live scrape 2026-06-15 via Playwright (Ashby board) — real posting, verbatim
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**Date:** 2026-06-15
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**Score:** 87.2 / 100
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---
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## Domain-Specialist Lens (from session file + JD)
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### Reviewer Persona
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A hands-on SRE / platform-engineering lead on Kraken's AI Infrastructure team (sits *inside* the Data org). Daily: Terraform/Nomad on EKS, GitOps (ArgoCD/Flux), model-serving + agent-execution layers, on-call for agentic workloads shipping 100+ versions/day across 25+ environments. Has screened many career SREs with Terraform + EKS + LLM-serving stacks. Eye-rolls at: analytics/BI framing, inflated solo-ownership of org-scale platforms, buzzword "agentic" with no production substance. Genuinely impressed by: someone who put real ML into a 24/7 line with no maintenance window, who thinks in platform/DX terms, and who is authentically crypto-native (rare in this applicant pool).
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### Company Context
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Payward (Kraken, NinjaTrader, Breakout, xStocks, CF Benchmarks). 15-yr crypto exchange, 10M+ users. This team builds/operates the orchestration, execution and model-serving layers under agent workflows — explicitly framed as a **platform engineering team** delivering APIs/SDKs/self-service so AI/Data/Eng teams consume agent infra *as a service*. Success is defined "beyond ops → developer experience, platform adoption, long-term scalability." Recently shipped an open-source crypto CLI built for AI agents (MCP server, Claude Code/Cursor compatible).
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### JD Vocabulary Extraction (ranked)
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| # | JD Term | Freq | Meaning at Kraken | Resume Match? |
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|---|---|---|---|---|
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| 1 | Site Reliability / SRE / Platform Engineer (title + 5+ yrs) | title+body | Production reliability + platform-building tenure | YES (header, summary, skills) |
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| 2 | Platform engineering / developer experience / self-service / APIs / SDKs consumed at scale | 4+ | The differentiating ask — build infra *as a service* for other eng teams | **PARTIAL** (self-service data products consumed by teams; no "DX"/"API"/"SDK" verbatim) |
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| 3 | ML infrastructure / model serving / MLOps | 3 | Compute/orchestration/serving for inference + agent execution | YES (summary, skills group, BS-1) |
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| 4 | Infrastructure as Code / Terraform | 2 | Provision AWS via Terraform | PARTIAL (IaC + CloudFormation; Terraform honest gap) |
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| 5 | Kubernetes + Docker | 2 | Containerized agent workloads | YES (×2 employers) |
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| 6 | Observability / monitoring / alerting / incident response / on-call | 3 | Reliability of agentic systems | YES (BS-4 stack, SW on-call) |
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| 7 | AWS | 2 | Cloud substrate | YES (SAA cert + bullets) |
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| 8 | Python + bash/shell | 2 | Primary scripting/lang | YES |
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| 9 | agentic / LLM-powered systems, guardrails/failure handling | 3 | Production-hardened agent infra | PARTIAL (AI workflows query data foundation; no agent-orchestration framework) |
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| 10 | High ownership, fast-moving 0→1 | 2 | Culture fit | YES (Component/App Owner, FC-1 0→1) |
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### Domain Vocabulary Map
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| Resume Currently Says | Could Say for THIS JD | Why |
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| "consumed directly by downstream teams and AI workflows" | "...as self-serve platform services with API access" | JD's #2 theme is DX / API-driven / self-service platform — currently bridged only halfway |
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| header "AI & Agent Infrastructure" | (fine) | Already strong — keep |
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| "self-service, governed data products" | keep + add "developer experience" once | "developer experience" is named twice in JD; not present verbatim anywhere |
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### Gap Ranking
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- **Fatal:** None. Kraken explicitly invites applicants who don't meet every req; no binary disqualifier present (Terraform is preferred, not gating; no degree/cert gate).
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- **Serious:** (1) Terraform — every "obvious fit" competitor has it; bridged honestly via CloudFormation in both docs. (2) "Developer experience / API-driven platform" vocabulary thin relative to how hard the JD leans on it. (3) No dedicated "SRE" job *title* in history (bridged via reliability/ownership content).
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- **Cosmetic:** LangGraph/CrewAI, Cloudflare Zero Trust, formal eval frameworks — most applicants also lack these; correctly not claimed.
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### Methodology Transfer Test
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| Achievement | How Kraken's expert reads it |
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|---|---|
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| BS-1 ML inference into 24/7 fab (Docker/K8s, no downtime) | "He's already done the hard part — hardening experimental ML into a no-maintenance-window production line. That's literally our charter." ✓ |
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| SW-7 self-service governed data products consumed by teams | "A platform other teams consume — the DX mindset we need, though I'd want to hear how API/self-serve it really is." ✓ (partial) |
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| BS-4 ELK/Grafana/Prometheus/Loki | "Real observability stack ownership, not a dashboard." ✓ |
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| SW Component Owner + on-call SLA / 2nd-3rd level | "Genuine reliability + incident-response ownership." ✓ |
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| SW-1 Teradata/Oracle → AWS as code (CloudFormation) | "IaC reflex is there; CloudFormation not Terraform, but the mental model transfers." ✓ |
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### Competitive Landscape
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- **Obvious fit:** Career SRE with dedicated title + Terraform + EKS + LLM-serving tooling (Kubeflow/Ray/Seldon), maybe prior crypto-firm experience.
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- **Our advantage:** Production ML-into-fab story, governed agentic data foundation, AND authentic crypto fluency (customer since 2017, holds BTC/ETH, writes Solidity) — a combination very few applicants have, and one Kraken explicitly values.
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- **Their advantage:** Terraform-by-name, dedicated SRE title, hands-on agent-orchestration frameworks. We bridge the first two honestly and concede the third.
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---
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## Five-Perspective Read-Through
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### ATS Robot (keyword scan)
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| JD keyword | Match |
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|---|---|
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| SRE / Site Reliability | YES (header, skills "SRE on-call") |
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| Platform Engineer / platform engineering | YES (header, summary) |
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| ML infrastructure / model serving / MLOps | YES |
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| developer experience | **NO (verbatim)** |
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| self-service infrastructure | YES ("self-service") |
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| APIs / SDKs | PARTIAL (no verbatim "API"/"SDK") |
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| Infrastructure as Code / IaC | YES |
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| Terraform | NO (honest gap; CloudFormation present) |
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| Kubernetes | YES |
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| Docker | YES |
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| AWS | YES |
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| bash/shell | YES |
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| Python | YES |
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| observability / monitoring / alerting | YES |
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| incident response / on-call | YES |
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| containerization & orchestration | YES |
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| Airflow / Kafka / Spark / data lake | YES (all four) |
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| CI/CD | YES |
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| agentic / LLM-powered | PARTIAL ("AI workflows/agents") |
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| high ownership | YES |
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**Match rate:** ~16.5 / 20 = **~83%** verbatim/semantic → **PASS**. Top truthfully-addable misses: "developer experience," "API."
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### Recruiter Glance (10 s)
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**Verdict: Forward.** Header tagline ("Site Reliability & Platform Engineer | Kubernetes · AWS · MLOps | AI & Agent Infrastructure") is dead-on target vocabulary. Current title (Staff Engineer, Switzerland's largest telco) signals level. Crypto + Kraken-since-2017 in the summary's last line is an instant "culture fit" flag for a crypto recruiter.
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### HR Screen (30 s)
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**Verdict: Phone screen.** Summary bridges cleanly (reliability/platform → ML inference → agent-consumable data foundation → crypto). 11+ yrs clears the 5+ bar comfortably. Skills group names all read target-domain. Remote/Switzerland-eligible matches posting.
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### Hiring Manager (2 min)
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**Verdict: Interview.**
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**Top 3 observations:**
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1. The Bosch "ML inference into a 24/7 fab, no downtime" bullet is the strongest single proof point for "bring production discipline to emerging tech" — exactly the JD thesis.
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2. Crypto-native authenticity + production platform ownership is a rare pairing; differentiator is visible without being gimmicky.
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3. Would probe: how API-driven / self-serve are the "data products" really, and how close is CloudFormation-to-Terraform in practice.
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**Predicted first interview question:** "Walk me through what 'self-service' meant on your Swisscom data products — what did consumers actually call, and how did you handle versioning and failure modes?"
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### Technical Reviewer (10 min)
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**Truthfulness:** Clean against session/KB.
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- Terraform never claimed; CloudFormation stated and bridged honestly in CL ("the model is identical and I would close that gap fast"). ✓
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- Data Mesh scoped correctly ("within Swisscom's company-wide Data Mesh") — no solo-ownership inflation. ✓
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- LangChain/LangGraph absent. ✓ Verified GenAI toolchain not over-stated.
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- Security Champion framed as a role with year window (2025/26), not an award. ✓
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- One **minor** wording mismatch: summary says "AI agents consume," resume bullet says "AI workflows," CL says "agentic AI workflows query." The agent-consumption claim is the strongest phrasing of a real-but-emerging reality. Align toward "AI workflows / agents query" for airtight defensibility (Tier 2).
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**Consistency:** Dates, titles, metrics consistent across resume + CL. No contradictions.
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---
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## Eight-Dimension Scoring
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| Dimension | Score | Weight | Weighted | Notes |
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|---|---|---|---|---|
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| ATS Keywords | 8.5/10 | 15% | 1.28 | ~83% match; misses "developer experience"/"API" verbatim, Terraform (honest) |
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| Summary | 9.0/10 | 10% | 0.90 | Strong bridge, crypto hook, target vocab; "AI agents consume" slightly ahead of bullets |
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| Skills Section | 9.0/10 | 10% | 0.90 | Group names all on-target; crypto/Web3 group is a smart differentiator |
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| Bullet Quality | 8.5/10 | 25% | 2.13 | BS-1 + SW reliability bullets excellent; DX/API/platform-adoption angle under-stated for a "platform engineering" team |
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| Publications | N/A (8.5 proxy) | 10% | 0.85 | No pubs expected for industry SRE; certs (AWS SAA active, AI Eng) carry credibility |
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| Narrative Coherence | 9.0/10 | 15% | 1.35 | Reliability → ML-infra → agentic data → crypto thread is tight and reader-legible |
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| Page Fill & Visual | 9.0/10 | 5% | 0.45 | 2 clean pages, page 2 ~70% filled, no orphans, compiles clean |
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| Credibility Signals | 8.5/10 | 10% | 0.85 | Component/App Owner titles, AWS SAA, CNN/BBC scale, 24/7 fab; no dedicated SRE title |
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| **Total** | | **100%** | **87.2** | |
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*(Publications dimension scored as a neutral proxy since the role is industry SRE with no publication expectation; certs assessed in its place.)*
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---
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## Interview Likelihood
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| Reader | Probability | Key Factor |
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|--------|------------|------------|
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| ATS | 90% PASS | 83% keyword match; crypto + K8s + AWS + observability all hit |
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| Recruiter (10s) | 85% Forward | On-target tagline + Staff title + crypto-since-2017 |
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| HR (30s) | 85% Phone screen | 11+ yrs clears 5+ bar; clean bridge summary |
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| Hiring Manager (2m) | 70% Interview | Bosch ML-into-fab + crypto authenticity; tempered by no Terraform / no SRE title |
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| Technical Panel (10m) | 65% Yes | Real production discipline; will probe DX/API depth + Terraform transfer |
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**Ceiling:** Current **87.2** → with Tier 1 applied **~88.5** → hard ceiling **~90** (capped by: no Terraform-by-name, no dedicated SRE title, no agent-orchestration framework — all structural, all honest gaps). What would close the last gap: hands-on Terraform + one LangGraph/CrewAI project. Not worth fabricating; the crypto + production-ML edge is the real lever.
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---
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## Actionable Improvements
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### Tier 1 (HIGH — worth doing, ~+1.3 total)
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1. **Inject "developer experience / API-driven" into the platform bullet (SW-7, bullet 2).** The JD names "developer experience" twice and "APIs/SDKs consumed by engineering teams" as the team's defining purpose, yet neither phrase appears. *Current:* "...consumed directly by downstream teams and AI workflows." *Proposed:* rework to surface self-serve + API/contract consumption and developer experience (e.g., "...as self-serve platform services other teams discover and consume via governed APIs/contracts"). Keep it honest to data-product reality. **+~0.8 (ATS + HM platform-thinking signal).**
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2. **Add "developer experience (DX)" to a skills line** (Containers/CI-CD or a platform descriptor) so the verbatim term lands for ATS without overclaiming. **+~0.5.**
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### Tier 2 (MEDIUM — optional, ~+0.5)
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1. **Align the agent-consumption phrasing.** Summary "AI agents consume" → "AI workflows and agents query/consume" to match the resume bullet and CL, tightening defensibility. **+~0.3.**
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2. **Bullet 7 (Security Champion) is 187 chars (2 under target)** — pad slightly or leave; cosmetic. **+~0.2.**
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### Tier 3 (COSMETIC — skip)
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1. Minor: consider naming "self-service / paved-road" idiom once — diminishing returns; "self-service" already present.
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### Verdict
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**Apply Tier 1 (DX/API vocabulary) — it directly addresses the one theme the JD weights most and the resume under-serves. Tier 2 alignment is a nice-to-have. Package is submit-ready at 87; Tier 1 nudges it to ~88.5.** Do NOT chase Terraform/LangGraph by fabrication — the honest crypto + production-ML positioning is the winning angle.
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---
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## Cover Letter Critique
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**Type:** Industry. **Word count:** 299 (target 250–300 — at the top edge, acceptable). **1 page.**
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- **6A Anti-patterns:** PASS. Opens with the Kraken AI-agent CLI / MCP shipment (specific, not generic). No bullet-rehash. Strongest hook (production-discipline + crypto) in P1. No defensive/apologetic framing — the CloudFormation-vs-Terraform line is confident, not apologetic. Active close ("I would welcome the chance to talk about keeping your agent infrastructure reliable as it scales").
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- **6B Tailoring:** PASS. Names a specific Kraken product (open-source agent CLI + MCP server, Claude Code/Cursor). Uses JD terms beyond the resume (agentic systems, model serving, platform-as-a-service, reliability). References the team's actual charter.
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- **6C Industry checks:** PASS. Business/production value translated; "why crypto" is positive and authentic (BTC/ETH, Solidity, 15 yrs watching Kraken); jargon calibrated for a technical platform reader.
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- **6D CL ATS:** 6–7 high-priority JD terms supplement the resume (agentic, MCP, model serving, observability, IaC, on-call). Good.
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- **6E Structural:** PASS. ~299 words, results-driven tone, sentence-length variety, IaC pivot leads with capability not apology.
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- **6F Package cohesion:** PASS. Every CL claim traces to a resume bullet (K8s/CI-CD → SW; Teradata/Oracle→AWS as code → SW-1; Bosch ML + observability → BS-1/BS-4; Security Champion → SW-5; self-service data products → SW-7). No new unsupported claims. Resume stands alone without the CL.
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**AI-fingerprint scan (12-item):** PASS. No Tier-1 banned words; no -ing bullet endings; 1 em-dash (address block, not prose); no generic opener; varied sentence length; no metaphorical "landscape/journey." Notably avoids the banned "at the intersection of X and Y" despite the JD using it — good restraint.
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---
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## Interview Bridge Points
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| Resume Topic | Target Equivalent | Opening Line |
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|---|---|---|
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| Bosch ML inference into 24/7 fab | Hardening agent prototypes into production | "The maintenance-window discipline I used to put ML into a fab line is the same discipline agent infra needs once real money flows through it." |
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| SW-7 self-service data products | Platform-as-a-service / DX | "I built data products as a self-serve platform — discoverable, governed, consumed by teams I never met; the DX problem is the same for an agent platform." |
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| CloudFormation IaC | Terraform | "My IaC reflex is CloudFormation; the provisioning model, drift, and review gates map one-to-one to Terraform — I'd be productive in days." |
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| BS-4 ELK/Grafana/Prometheus/Loki | Observability for AI/ML workloads | "I instrumented a 24/7 line end-to-end; for agents I'd add inference latency, failure/recovery patterns, and eval signals on top of the same telemetry spine." |
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| Component/App Owner + on-call | Incident response for agentic systems | "I've owned 2nd/3rd-level on-call with SLAs; I think in error budgets and runbooks, not heroics." |
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| Solidity / crypto-native | Domain fluency | "I'm a Kraken user since 2017 and write Solidity for fun — I understand why reliability in this domain isn't optional." |
|
||||
| FC-1 first Jenkins CI/CD from zero | 0→1 platform-building | "I've stood up the first CI/CD a team ever had — I'm comfortable in the 0→1 platform phase your team lives in." |
|
||||
|
||||
---
|
||||
|
||||
*End of critique.*
|
||||
@@ -0,0 +1,43 @@
|
||||
\documentclass[11pt,a4paper,roman]{moderncv}
|
||||
\usepackage[english]{babel}
|
||||
\moderncvstyle{classic}
|
||||
\moderncvcolor{green}
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage{lmodern}
|
||||
\usepackage{ragged2e}
|
||||
\usepackage[scale=0.79]{geometry}
|
||||
\usepackage[version=4,arrows=pgf-filled]{mhchem}
|
||||
\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par}
|
||||
|
||||
% ========== CONTACT ==========
|
||||
\name{Dennis}{Thiessen}
|
||||
\address{Bern, Switzerland}
|
||||
\phone[mobile]{+41 795 955 585}
|
||||
\email{dennis@thiessen.io}
|
||||
% =============================
|
||||
|
||||
\begin{document}
|
||||
|
||||
\recipient{To}{Kraken (Payward)\\AI Infrastructure Team, Data Organization\\Remote --- Switzerland}
|
||||
\date{\today}
|
||||
\opening{Dear AI Infrastructure Team,}
|
||||
\makelettertitle
|
||||
|
||||
\begin{justify}
|
||||
When Kraken shipped its open-source CLI for AI agents in March, with a built-in MCP server that lets Claude Code and Cursor execute directly against live markets, it confirmed a bet I had been making as an engineer and Kraken customer since 2017: agentic systems only matter once someone makes them reliable in production. That is the work of the AI Infrastructure team, and the reason I am writing about the Site Reliability Engineer, AI Agents role.
|
||||
|
||||
For nearly three years at Swisscom, Switzerland's largest telco, I have built and operated Python services on Kubernetes with GitLab CI/CD, owning them from build through production rollout and on-call. Before that I migrated our legacy Teradata and Oracle ETL to a cloud-native AWS stack (S3, Glue, Athena with Iceberg, Airflow) provisioned as code. My IaC is CloudFormation rather than Terraform, but the model is identical and I would close that gap fast. I also own security and access controls for the Data Lake team as its Security Champion.
|
||||
|
||||
What fits me most here is turning experimental systems into production ones. At Bosch I containerized and orchestrated ML inference (Docker, Kubernetes, Ansible) into a 24/7 semiconductor fab with no maintenance windows, and built the observability stack (ELK, Grafana, Prometheus, Loki) around it. At Swisscom I now build the self-service, governed data products that downstream teams and agentic AI workflows query. Reliability, model serving, observability, and a platform other teams consume as a service are the problems your posting names.
|
||||
|
||||
Doing this for a crypto-native company is the part I would be most invested in. I hold BTC and ETH, write Solidity in my own time, and have watched Kraken operate through fifteen years of market cycles. I would welcome the chance to talk about keeping your agent infrastructure reliable as it scales.
|
||||
\end{justify}
|
||||
|
||||
\vspace{0.3cm}
|
||||
{Sincerely,\\
|
||||
Dennis Thiessen, M.Eng.\\
|
||||
Staff Data, Analytics \& AI Engineer\\
|
||||
Swisscom (Schweiz) AG}
|
||||
|
||||
\end{document}
|
||||
@@ -0,0 +1,163 @@
|
||||
\documentclass{resume}
|
||||
\usepackage{hyperref}
|
||||
\usepackage{enumitem}
|
||||
\usepackage{fontawesome}
|
||||
\usepackage{tikz}
|
||||
\usepackage{graphicx}
|
||||
\hypersetup{
|
||||
colorlinks = true,
|
||||
linkcolor = [rgb]{0.9,0.4,0.4},
|
||||
anchorcolor = [rgb]{0.9,0.4,0.4},
|
||||
citecolor = [rgb]{0.4,0.4,0.4},
|
||||
filecolor = [rgb]{0.4,0.4,0.4},
|
||||
urlcolor = [rgb]{0.0,0.0,0.99},
|
||||
}
|
||||
\usepackage{xcolor}
|
||||
\usepackage[utf8]{inputenc}
|
||||
\usepackage[T1]{fontenc}
|
||||
\usepackage{lmodern}
|
||||
\usepackage[version=4,arrows=pgf-filled]{mhchem}
|
||||
\usepackage[includefoot,left=0.5in,top=0.5in,right=0.5in,bottom=0.2in,textwidth=7.5in,textheight=10.8in]{geometry}
|
||||
\usepackage{fancyhdr}
|
||||
\pagestyle{fancy}
|
||||
\fancyhf{}
|
||||
\renewcommand{\headrulewidth}{0pt}
|
||||
\fancyfoot[R]{\hfill \thepage/\pageref{LastPage}}
|
||||
\newcommand{\tab}[1]{\hspace{.2667\textwidth}\rlap{#1}}
|
||||
\newcommand{\itab}[1]{\hspace{0em}\rlap{#1}}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% HEADER
|
||||
%----------------------------------------------------------------------------------------
|
||||
\name{Dennis Thiessen, M.Eng.}
|
||||
\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
|
||||
\address{dennis@thiessen.io \\ +41 795 955 585}
|
||||
\address{Bern, Switzerland $\vert$ German citizen $\vert$ Open to fully remote (Switzerland-based) across EU}
|
||||
\address{{Site Reliability \& Platform Engineer $\vert$ Kubernetes $\cdot$ AWS $\cdot$ MLOps $\vert$ AI \& Agent Infrastructure}}
|
||||
|
||||
|
||||
\begin{document}
|
||||
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% SUMMARY
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection}{Summary}
|
||||
Site reliability and platform engineer with 11+ years in production infrastructure. At Switzerland's largest telco I operate \textbf{Python} services on \textbf{Kubernetes} with \textbf{GitLab CI/CD}, keep business-critical pipelines healthy under on-call SLA, and build self-service, governed data products on \textbf{AWS} that other teams and AI agents consume. Earlier I moved \textbf{ML} inference into a 24/7 Bosch fab with \textbf{Docker} and Ansible, with \textbf{Grafana}/\textbf{Prometheus} observability. \textbf{AWS} Certified Solutions Architect and \textbf{Python} expert; crypto-native (Solidity) and Kraken user since 2017.
|
||||
\end{rSection}
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% TECHNICAL SKILLS — Format C, 5 groups (4-3-2-2-2)
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection}{Technical Skills}
|
||||
|
||||
\begin{skillgroup}{Cloud Platform \& Infrastructure as Code}
|
||||
\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, Step Functions, \textbf{Airflow}, CloudWatch, ECR/ECS)}
|
||||
\skilldash{\textbf{Infrastructure as Code} (\textbf{CloudFormation}), serverless and event-driven architecture, AWS SAA-certified}
|
||||
\skilldash{Cloud-native application delivery, multi-service platform operation, scalability and cost awareness}
|
||||
\skilldash{Linux, networking fundamentals, Bash / shell scripting, Git, software architecture (iSAQB)}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{Containers, CI/CD \& Observability}
|
||||
\skilldash{\textbf{Kubernetes}, \textbf{Docker}, Ansible: containerized application deployment and orchestration}
|
||||
\skilldash{\textbf{GitLab CI/CD}, Jenkins: build, test and deploy automation, quality gates, DevSecOps}
|
||||
\skilldash{ELK Stack, \textbf{Grafana}, \textbf{Prometheus}, Loki: monitoring, alerting, SRE on-call, incident response}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{ML Infrastructure \& Data Engineering}
|
||||
\skilldash{\textbf{ML} inference / model serving in production, MLOps, containerized model deployment (\textbf{Docker}/\textbf{K8s}, Ansible)}
|
||||
\skilldash{\textbf{Kafka}, \textbf{Airflow}, PySpark / Apache Spark, ETL/ELT, Data Mesh / data products, data governance}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{Programming \& Crypto / Web3}
|
||||
\skilldash{\textbf{Python} (expert), Java, SQL, Bash, JavaScript/TypeScript, FastAPI, Pandas, pytest}
|
||||
\skilldash{\textbf{Solidity} / smart contracts, on-chain / Web3 fundamentals, EVM tooling, blockchain (personal projects)}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{Certifications}
|
||||
\skilldash{\textbf{AWS Certified Solutions Architect -- Associate} (active to Sep 2027), Data Engineering with AWS (Udacity)}
|
||||
\skilldash{iSAQB CPSA -- Foundation (2016), ITIL Foundation (2016), IBM AI Engineering Specialization}
|
||||
\end{skillgroup}
|
||||
|
||||
\end{rSection}
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% PROFESSIONAL EXPERIENCE
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection}{Professional Experience}
|
||||
|
||||
% --- Swisscom (Oct 2023 -- Present) — SW-3, SW-7, SW-1, SW-2, SW-4, SW-6 ---
|
||||
\begin{rSubsection}{Platform \& Reliability Engineering: Kubernetes, AWS \& Self-Service Data Products}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland}
|
||||
\item Build, deploy and operate \textbf{Python} services on \textbf{Kubernetes} with \textbf{GitLab CI/CD}, owning containerized delivery from build and test through production rollout and on-call operation in an agile DevOps team.
|
||||
\item Build self-service, governed data products with metadata management within Swisscom's company-wide \textbf{Data Mesh} on \textbf{AWS} (Glue, Athena, CloudFormation, CI/CD), consumed directly by downstream teams and AI workflows.
|
||||
\item Migrated Swisscom's legacy Teradata/Oracle ETL to a cloud-native \textbf{AWS} platform (S3, Glue, Athena/Iceberg, Redshift, \textbf{Airflow}) provisioned as code with \textbf{CloudFormation}, for serverless ML and analytics workloads.
|
||||
\item Own business-critical Fulfillment and Product Analysis pipelines (Oracle, \textbf{Kafka} to Teradata in \textbf{Python}) as Component Owner, enforcing data governance and SLAs under 2nd/3rd-level on-call and incident response.
|
||||
\item Deliver data products and dashboards for B2B product teams and automate recurring workflows in \textbf{Python}, running 3rd-level root cause analysis to keep platform pipelines reliable and available.
|
||||
\item Apply \textbf{PySpark} and distributed computing in the Swisscom Data Lake, extending \textbf{Python} pipeline capabilities to large-scale batch and streaming workloads for Fulfillment and Product Analysis data domains.
|
||||
\item Serve as the team's Security Champion (2025/26), owning \textbf{DevSecOps}, access controls, security compliance and deviation tracking for the Data Lake, with 100h annual cloud-security training.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Bosch (Feb 2020 -- Dec 2022) — BS-1, BS-4, BS-3, BS-2 ---
|
||||
\begin{rSubsection}{Production ML Infrastructure \& Observability in 24/7 Semiconductor Manufacturing}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
|
||||
\item Containerized and orchestrated production \textbf{ML} inference (\textbf{Docker}, \textbf{Kubernetes}, Ansible) for automated image-based defect classification in Bosch's 24/7 semiconductor fab across 300mm wafer lines without downtime.
|
||||
\item Delivered an anomaly-detection and observability stack (ELK with \textbf{Kafka} on \textbf{Docker}, plus \textbf{Grafana}, \textbf{Prometheus} and Loki), giving centralized monitoring and alerting for 24/7 manufacturing infrastructure.
|
||||
\item Served as Application Owner for the semiconductor analytics suite and upstream pipelines, defining SLOs, managing vendors, and delivering training and documentation to keep systems reliable 24/7.
|
||||
\item Developed data services in \textbf{Python}, Java and C\# over OracleDB and Hadoop/ImpalaSQL, giving analysis teams structured, reliable access to defect-management and process-optimization data in a high-throughput fab.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Fraunhofer (Sep 2018 -- Oct 2019) — FC-1, FC-3 ---
|
||||
\begin{rSubsection}{CI/CD Automation \& Containerized Microservices, Built from Zero}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany}
|
||||
\item Set up the team's first Jenkins \textbf{CI/CD} pipeline with quality gates independently, bringing build automation to the group; also developed the SCEDAS crew-scheduling system (C\#, .NET, MS SQL, Entity Framework).
|
||||
\item Built containerized microservices (Express.js, \textbf{Docker}, SQLite) for MISSION, a Fraunhofer maritime data-exchange platform connecting ports, operators and research partners across the logistics chain.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Vizrt (Jul 2017 -- May 2018) — VZ-1, VZ-2 ---
|
||||
\begin{rSubsection}{Distributed Real-Time Backend Engineering at Broadcast Scale}{\textcolor{black!60}{Jul 2017 -- May 2018}}{DevOps Engineer, Vizrt}{Bergen, Norway}
|
||||
\item Engineered distributed real-time video-transcoding backend components in \textbf{Python} (with legacy C++ modules) for Vizrt's broadcast platform, serving global media customers including CNN, BBC and Al Jazeera.
|
||||
\item Wrote an automated A/V integration and unit test suite in \textbf{Python} and wired quality gates into the \textbf{CI/CD} pipeline, which shortened the feedback loop for new features and raised release reliability.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Generali (May 2015 -- Jun 2017) — GN-1, GN-3, GN-2 ---
|
||||
\begin{rSubsection}{Test Automation, CI/CD Ownership \& Java Backend}{\textcolor{black!60}{May 2015 -- Jun 2017}}{IT Consultant, Generali Deutschland Informatik Services}{Hamburg, Germany}
|
||||
\item Introduced BDD test automation to Generali (Serenity-BDD, Selenium, JBehave), ran the PoC and took technical ownership, administered Jenkins \textbf{CI/CD} jobs, and trained teams across the Java Community.
|
||||
\item Developed Java/J2EE features for the PIA-Postkorb workflow portal, migrated WebServices to the XLDeploy process, and contributed to an Apache Camel / Spring Boot dispatcher integration PoC.
|
||||
\item Pioneered UIPath RPA at Generali GDIS, building PoCs and serving as the internal RPA contact for group companies, extending automation from test tooling into broader business process automation.
|
||||
\end{rSubsection}
|
||||
|
||||
|
||||
\end{rSection}
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% EDUCATION — FIXED
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection}{Education}
|
||||
{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Apr 2012 -- Oct 2013}}\\
|
||||
{Universit\"at der Bundeswehr M\"unchen}; thesis at Tongji University, Shanghai \hfill Thesis Grade: \textbf{1.0}\\
|
||||
{\small Thesis: \textit{Development of a Web-Based Remote Fault Diagnosis System} (Neural Networks, PSO, Fuzzy Logic)}
|
||||
|
||||
{B.Eng.\ Information and Telecommunication Technologies} \hfill {\textcolor{black!60}{Oct 2009 -- Oct 2012}}\\
|
||||
{Universit\"at der Bundeswehr M\"unchen}, Munich, Germany
|
||||
\end{rSection}
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% CERTIFICATIONS & AWARDS — FIXED
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection2}{Certifications \& Awards}
|
||||
\item \textbf{AWS Certified Solutions Architect -- Associate}, Amazon Web Services (2024, active until Sep 2027).
|
||||
\item \textbf{Data Engineering with AWS Nanodegree}, Udacity (2026). AWS data pipeline architecture.
|
||||
\item \textbf{iSAQB CPSA -- Foundation Level}, iSAQB (2016). Certified Professional for Software Architecture.
|
||||
\item \textbf{ITIL Foundation Certificate in IT Service Management}, PEOPLECERT / AXELOS (2016).
|
||||
\item \textbf{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML.
|
||||
\end{rSection2}
|
||||
|
||||
\begin{center}
|
||||
\vspace{0.1cm}
|
||||
\textit{Languages: German (native), English (fluent)}
|
||||
\end{center}
|
||||
|
||||
\end{document}
|
||||
@@ -0,0 +1,199 @@
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
% Medium Length Professional CV - RESUME CLASS FILE
|
||||
%
|
||||
% This template has been downloaded from:
|
||||
% http://www.LaTeXTemplates.com
|
||||
%
|
||||
% This class file defines the structure and design of the template.
|
||||
%
|
||||
% Original header:
|
||||
% Copyright (C) 2010 by Trey Hunner
|
||||
%
|
||||
% Copying and distribution of this file, with or without modification,
|
||||
% are permitted in any medium without royalty provided the copyright
|
||||
% notice and this notice are preserved. This file is offered as-is,
|
||||
% without any warranty.
|
||||
%
|
||||
% Created by Trey Hunner and modified by www.LaTeXTemplates.com
|
||||
%
|
||||
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
|
||||
|
||||
\ProvidesClass{resume}[2018/09/25 v1.0 Resume class]
|
||||
|
||||
\LoadClass[10pt, a4paper]{article} % Font size and paper type
|
||||
\usepackage{lastpage}
|
||||
\usepackage[parfill]{parskip} % Remove paragraph indentation
|
||||
\usepackage{array} % Required for boldface (\bf and \bfseries) tabular columns
|
||||
\usepackage{ifthen} % Required for ifthenelse statements
|
||||
\usepackage{enumitem}
|
||||
\pagestyle{empty} % Suppress page numbers
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% HEADINGS COMMANDS: Commands for printing name and address
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
\def \name#1{\def\@name{#1}} % Defines the \name command to set name
|
||||
\def \@name {} % Sets \@name to empty by default
|
||||
|
||||
\def \addressSep {$|$} % Set default address separator to a diamond
|
||||
|
||||
% One, two or three address lines can be specified
|
||||
\let \@addressone \relax
|
||||
\let \@addresstwo \relax
|
||||
\let \@addressthree \relax
|
||||
\let \@addressfour \relax
|
||||
|
||||
% \address command can be used to set the first, second, and third address (last 2 optional)
|
||||
\def \address #1{
|
||||
\@ifundefined{@addresstwo}{
|
||||
\def \@addresstwo {#1}
|
||||
}{
|
||||
\@ifundefined{@addressthree}{
|
||||
\def \@addressthree {#1}
|
||||
}{
|
||||
\@ifundefined{@addressfour}{
|
||||
\def \@addressfour {#1}
|
||||
} {\def \@addressone {#1}
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
% \printaddress is used to style an address line (given as input)
|
||||
\def \printaddress #1{
|
||||
\begingroup
|
||||
\def \\ {\addressSep\ }
|
||||
{#1}
|
||||
% \centerline{#1}
|
||||
\endgroup
|
||||
\par
|
||||
% \addressskip
|
||||
}
|
||||
|
||||
% \printname is used to print the name as a page header
|
||||
\def \printname {
|
||||
\begingroup
|
||||
% \MakeUppercase
|
||||
{\namesize\bf \@name} \hfil
|
||||
% \hfil{\MakeUppercase{\namesize\bf \@name}}\hfil
|
||||
\nameskip\break
|
||||
\endgroup
|
||||
}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% PRINT THE HEADING LINES
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
\let\ori@document=\document
|
||||
\renewcommand{\document}{
|
||||
\ori@document % Begin document
|
||||
% \begin{center}
|
||||
\printname % Print the name specified with \name
|
||||
\@ifundefined{@addressone}{}{ % Print the first address if specified
|
||||
\printaddress{\@addressone}}
|
||||
\@ifundefined{@addresstwo}{}{ % Print the second address if specified
|
||||
\printaddress{\@addresstwo}}
|
||||
\@ifundefined{@addressthree}{}{ % Print the third address if specified
|
||||
\printaddress{\@addressthree}}
|
||||
\@ifundefined{@addressfour}{}{ % Print the third address if specified
|
||||
\printaddress{\@addressfour}}
|
||||
|
||||
% \end{center}
|
||||
}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% SECTION FORMATTING
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
% Defines the rSection environment for the large sections within the CV
|
||||
\newenvironment{rSection}[1]{ % 1 input argument - section name
|
||||
\sectionskip
|
||||
{\bf #1}
|
||||
% \MakeUppercase{\bf #1} % Section title
|
||||
\sectionlineskip
|
||||
\hrule % Horizontal line
|
||||
\begin{list}{}{ % List for each individual item in the section
|
||||
\setlength{\leftmargin}{0.50em} % Margin within the section
|
||||
}
|
||||
\item[]
|
||||
}{
|
||||
\end{list}
|
||||
}
|
||||
|
||||
\newenvironment{rSection2}[1]{ % 1 input argument - section name
|
||||
\sectionskip
|
||||
{\bf #1} % Section title
|
||||
\sectionlineskip
|
||||
\hrule % Horizontal line
|
||||
\medskip
|
||||
\begin{list}{$\bullet$}{\setlength{\leftmargin}{1.5em}}
|
||||
\itemsep -0.3em \vspace{-0.5em} % Compress items in list together for aesthetics
|
||||
}{
|
||||
\end{list}
|
||||
\vspace{0.5em}
|
||||
}
|
||||
|
||||
\newenvironment{rSection3}[1]{ % 1 input argument - section name
|
||||
\sectionskip
|
||||
{\bf #1} % Section title
|
||||
\sectionlineskip
|
||||
\hrule % Horizontal line
|
||||
\medskip
|
||||
\begin{enumerate}[]{\setlength{\leftmargin}{1.5em}}
|
||||
\itemsep -0.3em \vspace{-0.5em} % Compress items in list together for aesthetics
|
||||
}{
|
||||
\end{enumerate}
|
||||
\vspace{0.5em}
|
||||
}
|
||||
%----------------------------------------------------------------------------------------
|
||||
% WORK EXPERIENCE FORMATTING
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
\newenvironment{rSubsection}[4]{ % 4 input arguments - company name, year(s) employed, job title and location
|
||||
{\bf #1} \hfill {#2} % Bold company name and date on the right
|
||||
\ifthenelse{\equal{#3}{}}{}{ % If the third argument is not specified, don't print the job title and location line
|
||||
\\
|
||||
{\em #3} \quad {\em #4} % Italic job title and location
|
||||
}\smallskip
|
||||
\begin{list}{$\cdot$}{\leftmargin=1.5em} % \cdot used for bullets, no indentation
|
||||
\itemsep -0.2em \vspace{-0.2em} % Compress items in list together for aesthetics
|
||||
}{
|
||||
\end{list}
|
||||
\vspace{0.2 em} % Some space after the list of bullet points
|
||||
}
|
||||
|
||||
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% FORMAT C SKILLS COMMANDS
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
% Skills group environment: \begin{skillgroup}{Group Name} ... \end{skillgroup}
|
||||
% Renders bold header + indented dash sub-items. Each \skilldash = exactly 1 rendered line.
|
||||
\newenvironment{skillgroup}[1]{%
|
||||
\textbf{#1}\par\nopagebreak%
|
||||
\vspace{-\parskip}%
|
||||
\begin{list}{--}{\leftmargin=0.8em \labelsep=0.3em \itemsep=0pt \topsep=0.1em \parsep=0pt \partopsep=0pt}%
|
||||
}{%
|
||||
\end{list}%
|
||||
\vspace{-\parskip}\vspace{0.45em}%
|
||||
}
|
||||
|
||||
% Single dash sub-item within a skillgroup. Content must fit 1 rendered line.
|
||||
% Char limit: 119 - (0.5 x bold_char_count) at 10pt
|
||||
\newcommand{\skilldash}[1]{\item #1}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% EXPERIENCE SUB-THEME COMMAND
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
% Sub-theme underline header within rSubsection
|
||||
\newcommand{\subtheme}[1]{\item[] \underline{#1}}
|
||||
|
||||
% The below commands define the whitespace after certain things in the document - they can be \smallskip, \medskip or \bigskip
|
||||
\def\namesize{\huge} % Size of the name at the top of the document
|
||||
\def\addressskip{\smallskip} % The space between the two address (or phone/email) lines
|
||||
\def\sectionlineskip{\medskip} % The space above the horizontal line for each section
|
||||
\def\nameskip{\medskip} % The space after your name at the top
|
||||
\def\sectionskip{\medskip} % The space after the heading section
|
||||
@@ -0,0 +1,157 @@
|
||||
# Session: Kraken (Payward) — Site Reliability Engineer, AI Agents
|
||||
|
||||
## JD Info
|
||||
- **File:** JDs/kraken_sre_ai_agents.txt
|
||||
- **JD source:** live scrape 2026-06-15 via Playwright (Ashby board)
|
||||
- **URL:** https://jobs.ashbyhq.com/kraken.com/c331de1b-b75a-48f5-9d19-0e56ccb935ab
|
||||
- **Role:** Site Reliability Engineer – AI Agents (AI Infrastructure team, within Data org)
|
||||
- **Company:** Kraken / Payward — crypto exchange, 15 yrs, 10M+ users, 70+ countries
|
||||
- **Bundle:** Data Platform / Infra (primary) + ML/AI Engineer (secondary bridge)
|
||||
- **Format:** Resume (2-page, resume.cls) + 1-page cover letter
|
||||
- **Salary/Details:** Not stated (Kraken does not publish CH band). Remote, Switzerland-eligible. Verify clears 180k+ all-in before final send.
|
||||
|
||||
## JD Analysis
|
||||
### Requirements
|
||||
| # | Requirement | Match | Evidence |
|
||||
|---|-------------|-------|----------|
|
||||
| 1 | 5+ yrs SRE / Infra / Platform Engineer in production | Direct | Swisscom DevOps/K8s/on-call (2023–present) + Bosch App Owner/infra (2020–22) = 5+ yrs combined platform ownership |
|
||||
| 2 | Hands-on ML infra / model serving / MLOps in production | Direct | BS-1: containerized + orchestrated ML inference (Docker/K8s/Ansible) into 24/7 production line |
|
||||
| 3 | Building developer platforms, internal tooling, APIs/SDKs consumed at scale | Bridge (HIGH) | SW-7: self-serve governed data products consumed by downstream teams; BS-2: multi-language data services consumed by analysis teams. Not literally "SDKs" — frame as platform/services consumed by eng teams |
|
||||
| 4 | Platform-eng principles: DX, self-service infra, API-driven design | Bridge (HIGH) | SW-7: decentralized Data Mesh = self-serve, discoverable, governed data-products model |
|
||||
| 5 | IaC, particularly Terraform | Bridge (MED) | SW-1/SW-7: CloudFormation IaC (full provisioning). Terraform NOT used — frame as transferable IaC, never claim Terraform. (User confirmed.) |
|
||||
| 6 | Containerization + orchestration (Kubernetes, Docker) | Direct | SW-3 (K8s+GitLab) + BS-1 (K8s/Docker/Ansible) — two employers |
|
||||
| 7 | Cloud infra, preferably AWS | Direct | SW-1 (S3/Glue/Athena/Redshift/Airflow/CloudFormation), SW-7 (AWS Data Mesh). AWS SAA cert. |
|
||||
| 8 | Strong scripting (bash/shell) + Python | Direct | Python primary across SW-2/3, BS-2; bash/shell in CI/CD ops |
|
||||
| 9 | Observability, monitoring, alerting systems | Direct | BS-4: ELK + Kafka + Grafana + Prometheus + Loki full stack |
|
||||
| 10 | Incident response + on-call rotations | Direct | SW-2 (on-call SLA, 2nd/3rd-level), BS-3 (App Owner SLOs, 24/7) |
|
||||
| 11 | Collaboration across data/AI/eng teams | Direct | SW-4 (PO/stakeholder), BS-3 (cross-team adoption) |
|
||||
| 12 | High ownership in fast-moving production | Direct | Component Owner (Swisscom) + Application Owner (Bosch) titles |
|
||||
| NTH | Infra for agent-based / LLM systems | Bridge (MED) | SW-7: agentic data foundation (governed data products agents query); LiteLLM gateway |
|
||||
| NTH | Agent orchestration frameworks (LangGraph, CrewAI) | **Gap** | Do NOT claim. Config bans LangChain/LangGraph fabrication. Verified toolchain: Kiro, Copilot, LiteLLM, custom GPTs |
|
||||
| NTH | Data infra (Airflow, Kafka, Spark, data lake) | Direct | SW-1 (Airflow), SW-2 (Kafka), SW-6 (PySpark), Swisscom Data Lake |
|
||||
| NTH | CI/CD + deployment automation for AI/ML | Direct | SW-3 (GitLab CI/CD), FC-1 (Jenkins from zero) |
|
||||
| NTH | Eval frameworks / model perf monitoring at scale | Bridge (LOW) | BS-4 monitoring/anomaly detection — weak; don't oversell |
|
||||
| NTH | 0→1 / platform-building teams | Bridge (MED) | FC-1 (introduced CI/CD from zero), BS-4 (observability PoC) |
|
||||
| NTH | Cloudflare ecosystem (Zero Trust, networking) | Gap | Minor; omit |
|
||||
|
||||
### ATS Keywords
|
||||
- **Platform/Infra:** SRE, Site Reliability, Platform Engineering, Infrastructure as Code, IaC, Kubernetes, Docker, AWS, CI/CD, self-service infrastructure, developer experience
|
||||
- **ML/AI:** ML infrastructure, model serving, MLOps, model inference, agentic, LLM-powered systems, AI infrastructure
|
||||
- **Data:** data infrastructure, Airflow, Kafka, Spark, data lake, data products, data engineering
|
||||
- **Reliability:** observability, monitoring, alerting, incident response, on-call, SLO/SLA, runbooks
|
||||
- **Tools:** Terraform (IaC — bridge via CloudFormation), Kubernetes, Docker, AWS, GitLab CI/CD, Grafana, Prometheus, ELK, Python, bash
|
||||
- **Soft:** high ownership, cross-team collaboration, fast-moving / high-stakes production, platform adoption
|
||||
|
||||
### Gap Assessment
|
||||
- **Direct:** SRE/platform tenure, production ML serving (BS-1), K8s+Docker, AWS, Python/bash, observability stack, on-call/incident response, data infra (Kafka/Airflow/Spark), CI/CD, ownership mindset
|
||||
- **Bridge:** dev-platform/SDK consumption (SW-7/BS-2, HIGH); platform-eng DX/self-service (SW-7, HIGH); IaC→Terraform (CloudFormation, MED); agent/LLM infra (SW-7 agentic foundation, MED); 0→1 (FC-1, MED)
|
||||
- **Gap (do NOT claim):** Agent orchestration frameworks (LangGraph/CrewAI), Cloudflare Zero Trust, formal LLM eval frameworks. Honest gaps — Kraken explicitly invites applicants who don't meet every req.
|
||||
|
||||
## Company Context
|
||||
- **Mission:** "Building the Future of Open Finance" — Payward (Kraken, NinjaTrader, Breakout, xStocks, CF Benchmarks). 15 yrs building globally accessible financial infrastructure; advance an open global financial system.
|
||||
- **This role:** AI Infrastructure team sits *within the Data org* — builds/operates/scales the systems powering AI agents in production (internal + external). Owns orchestration, execution, model-serving layers under agentic workflows. Explicitly a **platform engineering team**: builds APIs/SDKs/platform capabilities so AI/Data/Eng teams consume agent infra as a service. Success = beyond ops → DX, platform adoption, long-term scalability.
|
||||
- **Real stack (web research):** EKS/Kubernetes, Terraform + Nomad IaC, AWS + on-prem private connectivity, GitOps (ArgoCD/Flux evaluated), Docker, Cilium CNI. Ships 100+ versions/day to 25+ environments across 10+ countries. Engineering blog at engineering.kraken.tech.
|
||||
- **Agentic context:** Kraken shipped the first crypto CLI built for AI agents (open-source, MCP server, Claude Code/Cursor compatible, 134 commands). Building an "AI-native finance operating system" / agentic finance layer across 70+ regulated entities.
|
||||
- **Culture:** Crypto-native, high-ownership, fast-moving 0→1, fully remote/global, ships fast. "Apply even if you don't meet all reqs, especially if passionate about crypto."
|
||||
- **"Why them" angle:** Dennis is a Kraken customer since 2017, holds BTC+ETH, writes Solidity in free time — genuine crypto-native fit. Pair with production platform/ML-infra ownership. He brings the production discipline (on-call, observability, governed data foundation) that the JD says it needs to "harden agent infrastructure."
|
||||
|
||||
## Framing Strategy
|
||||
- **Lead narrative:** "Production-discipline platform engineer who has put ML inference into a 24/7 line and now owns the cloud-native, governed data foundation that agentic workflows run on — exactly the intersection of data infrastructure and applied AI this team works at."
|
||||
- **Reframing map:**
|
||||
- Data Mesh / data products → "self-service platform consumed by engineering teams" (DX/API-driven platform)
|
||||
- ML inference containerization (BS-1) → "ML infrastructure / model serving in production"
|
||||
- CloudFormation → "Infrastructure as Code (IaC)" (transferable to Terraform; never claim Terraform)
|
||||
- ELK/Grafana/Prometheus → "observability, monitoring & alerting for production systems"
|
||||
- Component/Application Owner + on-call → "incident response, SLOs, reliability ownership"
|
||||
- SW-7 governed data foundation → "the data layer agentic workflows query" (agentic bridge)
|
||||
- **Emphasize:** Kubernetes (×2 employers), production ML serving (BS-1), AWS, observability stack, on-call/SLO ownership, self-serve data platform, IaC
|
||||
- **Downplay:** semiconductor-domain specifics (keep ML-infra angle, drop fab jargon); BI/analytics framing; pure-DE "Fulfillment domain" context (lead with platform/reliability instead)
|
||||
- **CL hooks:** (1) Kraken CLI for AI agents + MCP/agentic finance layer ↔ Dennis's governed data foundation for agentic workflows; (2) "intersection of data infrastructure and applied AI" = literal description of his SW-7 + BS-1 combo; (3) crypto-native: customer since 2017, holds BTC+ETH, Solidity; (4) production discipline (on-call, observability) for hardening agent infra.
|
||||
- **User directives:** IaC framed as transferable, NO Terraform by name. Build Kraken now; Google Senior DE shelved for later.
|
||||
|
||||
## Critique Context
|
||||
- **Reviewer persona:** A hands-on SRE/platform eng hiring manager on the AI Infrastructure team (Data org). Cares about production discipline, real K8s/AWS/IaC/observability ownership, MLOps reality, and whether candidate thinks in platform/DX terms — not buzzwords. Crypto-curious is a plus. Bored by analytics/BI framing and by inflated solo-ownership claims.
|
||||
- **Competitive landscape:** Other applicants = career SREs/platform engineers with Terraform + EKS + MLOps tooling (Kubeflow/Ray/Seldon) and possibly crypto-firm experience. The "obvious fit" has dedicated SRE title + Terraform + LLM-serving stack. Dennis's edge: production ML-into-fab story, governed agentic data foundation, AND authentic crypto fluency — a combination few have.
|
||||
- **Domain vocabulary (insider):** self-service platform, golden paths, paved road, SLO/error budget, GitOps, IaC drift, model serving, inference latency, agent orchestration, observability/telemetry, on-call rotation, runbooks, MCP, agentic workflows.
|
||||
|
||||
## Cover Letter Plan
|
||||
- **Institution type:** Crypto-native, fast-moving, fully-remote tech company; platform/SRE audience
|
||||
- **Paragraph count:** 4 paragraphs, ~270 words
|
||||
- **P1 hook:** Kraken CLI for AI agents / MCP + agentic finance layer → "intersection of data infrastructure and applied AI" mirrors my own work; + crypto-native (customer since 2017)
|
||||
- **P2-P3 evidence:** P2 = production platform/reliability (K8s ×2, AWS migration + IaC, observability stack, on-call/SLO ownership). P3 = ML-infra + agentic data foundation (BS-1 production ML serving + SW-7 governed self-serve data products that agentic workflows query)
|
||||
- **Domain pivot:** IaC (CloudFormation) transferable to their Terraform/Nomad stack — state honestly, don't overclaim
|
||||
- **Jargon level:** Technical (platform/SRE audience)
|
||||
- **"Why them" hook:** Crypto-native + production discipline = bring reliability engineering to emerging agentic tech, which the JD explicitly asks for
|
||||
|
||||
## Bullet Plan (CONFIRMED 2026-06-15)
|
||||
|
||||
Crypto decision: signal ON résumé (Skills group 4 = "Programming & Crypto/Web3": Solidity, smart contracts, on-chain fundamentals — free-time/personal, honest per [[user_crypto]]) + full "why them" story in CL. Generali: keep all 3 (page fill, proven).
|
||||
|
||||
### Position 1 — Swisscom (6 bullets, 12 lines)
|
||||
| # | ID | Achievement | Variant | Rationale |
|
||||
|---|-----|------------|---------|-----------|
|
||||
| 1 | SW-3 | K8s + GitLab CI/CD (lead) | 2L | Platform/SRE lead — K8s, CI/CD |
|
||||
| 2 | SW-7 | Data Mesh self-serve data products consumed by eng teams + agentic data layer | 2L | Platform-as-service + agentic bridge. SCOPE object: "within Swisscom's company-wide Data Mesh" |
|
||||
| 3 | SW-1 | AWS migration + CloudFormation IaC | 2L | AWS + IaC (transferable to Terraform; never name Terraform) |
|
||||
| 4 | SW-2 | Component Owner, on-call SLA | 2L | Reliability / incident response |
|
||||
| 5 | SW-4 | Data products + automation + 3rd-level RCA | 2L | Reliability / RCA |
|
||||
| 6 | SW-6 | PySpark distributed processing | 2L | Spark (named NTH) + data infra |
|
||||
|
||||
### Position 2 — Bosch (4 bullets, 8 lines)
|
||||
| # | ID | Achievement | Variant | Rationale |
|
||||
|---|-----|------------|---------|-----------|
|
||||
| 1 | BS-1 | ML inference containerization, 24/7 prod (flagship) | 2L | "ML infra / model serving in production" — core req |
|
||||
| 2 | BS-4 | ELK + Grafana + Prometheus + Loki observability | 2L | Observability/monitoring/alerting |
|
||||
| 3 | BS-3 | Application Owner — SLOs, reliability | 2L | SLO/reliability ownership |
|
||||
| 4 | BS-2 | Multi-language data services consumed by teams | 2L | Platform consumers bridge |
|
||||
|
||||
### Position 3 — Fraunhofer (2 bullets, 4 lines)
|
||||
| # | ID | Achievement | Variant | Rationale |
|
||||
|---|-----|------------|---------|-----------|
|
||||
| 1 | FC-1 | First Jenkins CI/CD from zero (0→1) + SCEDAS | 2L | "0→1 / platform-building" (named NTH) |
|
||||
| 2 | FC-3 | Containerized microservices (Express.js/Docker) | 2L | Docker/microservices |
|
||||
|
||||
### Position 4 — Vizrt (2 bullets, 4 lines)
|
||||
| # | ID | Achievement | Variant | Rationale |
|
||||
|---|-----|------------|---------|-----------|
|
||||
| 1 | VZ-1 | Distributed real-time backend (Python/C++) | 2L | Distributed backend; CNN/BBC scale |
|
||||
| 2 | VZ-2 | A/V test suite + CI/CD quality gates | 2L | CI/CD |
|
||||
|
||||
### Position 5 — Generali (3 bullets, 6 lines)
|
||||
| # | ID | Achievement | Variant | Rationale |
|
||||
|---|-----|------------|---------|-----------|
|
||||
| 1 | GN-1 | BDD + CI/CD ownership (Jenkins) | 2L | CI/CD initiative |
|
||||
| 2 | GN-3 | Java/J2EE, XLDeploy, Camel/Spring Boot | 2L | Java breadth / page fill |
|
||||
| 3 | GN-2 | UIPath RPA PoC | 2L | Page fill |
|
||||
|
||||
**Budget:** 17 variable bullets (34 rendered lines) + Skills 13 lines (4-3-2-2-2). Matches proven QuantCo 2-page fill. Verify at page-fill gate.
|
||||
|
||||
## Output Files
|
||||
- Resume: `output/Kraken_SRE_AI_Agents/e2e_kraken_sre_ai_agents_resume.tex` (+ .pdf, 2 pages)
|
||||
- Cover Letter: `output/Kraken_SRE_AI_Agents/e2e_kraken_sre_ai_agents_cover_letter.tex` (+ .pdf, 1 page, ~287 words)
|
||||
- Critique: PENDING
|
||||
|
||||
## Status
|
||||
- Phase 0: DONE
|
||||
- Phase 1: DONE (17 confirmed → 18 generated; added SW-5 security bullet to close JD access-controls req + page fill)
|
||||
- Phase 2 Resume:
|
||||
- Summary: DONE (548 chars)
|
||||
- Skills: DONE (4-3-2-2-2, crypto/Web3 group added)
|
||||
- Position 1 Swisscom (7 bullets): DONE
|
||||
- Position 2 Bosch (4 bullets): DONE
|
||||
- Position 3 Fraunhofer (2 bullets): DONE
|
||||
- Position 4 Vizrt (2 bullets): DONE
|
||||
- Position 5 Generali (3 bullets): DONE
|
||||
- Compile: DONE (2 pages, MiKTeX)
|
||||
- Cover Letter: DONE (1 page, ~287 words; P1 hook = Kraken open-source AI-agent CLI + MCP, verified; crypto-native + production-discipline narrative; IaC honest CloudFormation-not-Terraform)
|
||||
- Critique: CURRENT (87.2/100, 2026-06-15). PASS all gates; CL 299w/1pg, resume 2pg clean. Tier 1 = inject "developer experience/API-driven" vocab into SW-7 platform bullet + a skills line (JD's most-weighted theme, under-served). Honest gaps: Terraform, dedicated SRE title, agent-orchestration frameworks (do NOT fabricate). Crypto + production-ML edge is the lever. Ceiling ~90.
|
||||
- **FINALIZED 2026-06-15** — submit-ready at 87.2, Tier 1 declined (finalize as-is). Submission PDFs: Dennis_Thiessen_Resume.pdf + Dennis_Thiessen_Cover_Letter.pdf
|
||||
- **SENT 2026-06-15** — applied via Ashby. Comp unknown — verify clears 180k+ at recruiter stage. Await response.
|
||||
- **Critique file:** output/Kraken_SRE_AI_Agents/critique_kraken_sre_ai_agents.md
|
||||
- **Before send:** verify comp clears 180k+ all-in (Kraken does not publish CH band)
|
||||
- Phase 2 Resume: PENDING
|
||||
- Cover Letter: PENDING
|
||||
- Critique: PENDING
|
||||
- **Next:** (Phase 1 — bullet plan, this session)
|
||||
- **Next CL:** /make-cl output/Kraken_SRE_AI_Agents/session_kraken_sre_ai_agents.md
|
||||
- **Next Critique:** /critique output/Kraken_SRE_AI_Agents/session_kraken_sre_ai_agents.md
|
||||
Reference in New Issue
Block a user