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feat(applications): submit Google Sr Data Engineer + Kraken SRE AI Agents (2026-06-15)

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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>
This commit is contained in:
Dennis Thiessen
2026-06-15 19:57:18 +02:00
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Site Reliability Engineer - AI Agents — Kraken (Payward)
JD source: live scrape 2026-06-15 via Playwright (Ashby board)
URL: https://jobs.ashbyhq.com/kraken.com/c331de1b-b75a-48f5-9d19-0e56ccb935ab
Location: Remote — Switzerland eligible (+ UK, EU, LATAM, others)
Employment: Full time · Remote · Engineering / SRE / DevOps
--- VERBATIM POSTING TEXT ---
Building the Future of Open Finance
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.
Before you apply, we encourage you to explore our culture page to understand what drives us and how we work.
The team
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.
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.
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.
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.
The opportunity
Design, build, and operate the infrastructure layer supporting AI agent workflows in production
Ensure reliability, scalability, and observability of agentic systems across internal and external products
Design and develop platform services, APIs, SDKs, and self-service capabilities that allow engineering teams to easily consume AI infrastructure and agent platform services
Manage and maintain the compute, orchestration, and serving infrastructure powering model inference and agent execution
Implement robust monitoring, alerting, and incident response procedures tailored to AI/ML workloads
Utilize Infrastructure as Code (IaC) tools such as Terraform to provision and manage cloud (AWS) infrastructure components
Build and maintain CI/CD pipelines that support rapid, reliable deployment of AI services and agent workflows
Define and implement guardrails, failure handling, and recovery patterns specific to agentic and LLM-powered systems
Collaborate with AI and Data Engineering teams to translate experimental agent prototypes into hardened production systems
Manage containerized workloads using Kubernetes, ensuring efficient deployment, scaling, and orchestration of AI services
Implement access controls and security best practices across AI infrastructure environments
Document architecture, runbooks, and best practices to support knowledge sharing across the team
What You Bring
5+ years of experience as a Site Reliability Engineer, Infrastructure Engineer, Platform Engineer, or similar role in a production environment
Hands-on experience supporting ML infrastructure, model serving, or MLOps workflows in production
Experience building developer platforms, internal tooling, APIs, or SDKs consumed by engineering teams at scale
Strong understanding of platform engineering principles, including developer experience, self-service infrastructure, and API-driven platform design
Proficiency with Infrastructure as Code tools, particularly Terraform
Experience with containerization and orchestration, particularly Kubernetes and Docker
Solid understanding of cloud infrastructure, preferably AWS
Strong scripting skills (bash/shell) and proficiency in at least one programming language (Python preferred)
Experience designing and operating observability, monitoring, and alerting systems
Experience implementing incident response procedures and participating in on-call rotations
Strong collaboration skills working across data, AI, and engineering teams
High ownership mindset in a fast-moving, high-stakes production environment
Nice to haves
Experience building or operating infrastructure for agent-based or LLM-powered systems
Familiarity with agent orchestration frameworks (e.g., LangGraph, CrewAI, or similar)
Background in data infrastructure, including familiarity with Airflow, Kafka, Spark, or data lake tooling
Experience with CI/CD pipelines and deployment automation for AI/ML workloads
Exposure to evaluation frameworks and model performance monitoring at scale
Experience working in fast-moving 0→1 environments or platform-building teams
Experience building SDKs, developer tooling, or internal platform products with a strong focus on usability and adoption
Experience with Cloudflare's cloud platform and product ecosystem, including networking, security, performance, and Zero Trust solutions
Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis.
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.