chore: add Swisscom SW-7 data mesh achievement, Google FDE drafts, scout perms
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
@@ -83,7 +83,16 @@
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"Bash(job_scout/.venv/Scripts/python.exe -c ' *)",
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"Bash(job_scout/.venv/Scripts/python.exe job_scout/scout.py --only=meta)",
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"Bash(job_scout/.venv/Scripts/python.exe job_scout/scout.py --only=cisco --include-weak)",
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"Bash(job_scout/.venv/Scripts/python.exe job_scout/scout.py --only=confluent)"
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"Bash(job_scout/.venv/Scripts/python.exe job_scout/scout.py --only=confluent)",
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"Bash(curl -s -A \"Mozilla/5.0\" \"https://coinbase.getro.com/jobs\")",
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"Bash(grep -oE '\"network\":\\\\{[^}]*\\\\}|\"networkId\":[0-9]+|\"network_id\":[0-9]+|\"id\":[0-9]+,\"name\":\"Coinbase\"|\"name\":\"Coinbase\"[^}]*')",
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"Bash(grep -oE '\\(algolia[^\"]{0,40}|\"appId\":\"[^\"]*\"|\"apiKey\":\"[^\"]*\"|\"indexName\":\"[^\"]*\"|[A-Z0-9]{8,12}-dsn\\\\.algolia\\)')",
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"Bash(curl -s -o /dev/null -w \"%{http_code}\\\\n\" -X POST \"https://api.getro.com/api/v2/collections/1625/search/jobs\" -H \"Content-Type: application/json\" -d '{\"hitsPerPage\":5}')",
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"Bash(.venv/Scripts/python.exe _inspect_bs.py)",
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"Bash(curl -s -m 20 -A Mozilla/5.0 -H 'X-Requested-With: XMLHttpRequest' 'https://bitcoin-suisse.onlyfy.jobs/candidate/job/ajax_list?display_length=50&page=1&sort=date&sort_dir=DESC&search=' -o /tmp/ajax.html)",
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"Bash(grep -oE 'icon-map-mark.{0,160}' /tmp/ajax.html)",
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"Bash(grep -oE 'job-title.*?icon-map-mark.{0,200}')",
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"Bash(grep -oiE '\\(icon-[a-z-]*\\).{0,80}\\(Zug|Switzerland|Schweiz|Copenhagen|Bratislava|Vaduz|Liechtenstein|Slovakia|Denmark\\)' /tmp/ajax.html)"
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]
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}
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}
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@@ -0,0 +1,31 @@
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Senior Forward Deployed Engineer, GenAI, Google Cloud
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Google — Go-To-Market team, Google Cloud
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Locations: Vienna, Austria; Zürich, Switzerland; Berlin, Germany; Hamburg, Germany; Munich, Germany
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Level: Mid
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URL: https://www.google.com/about/careers/applications/jobs/results/98998917743420102-senior-forward-deployed-engineer-genai-google-cloud
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Minimum qualifications:
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- Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
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- 6 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, Typescript or comparable languages.
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- Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
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- Experience designing and building AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
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- Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise-grade AI solutions.
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Preferred qualifications:
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- Master's degree or PhD in AI, Computer Science, or a related technical field.
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- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google's Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
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- Knowledge of large language model native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
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About the job:
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As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder bridging the gap between frontier AI products and production-grade reality for our customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer's environment.
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In this role, you will manage blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud's future product roadmap.
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It's an exciting time to join Google Cloud's Go-To-Market team, leading the AI revolution for businesses worldwide. We'll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform. We're a collaborative culture providing direct access to DeepMind's engineering and research minds.
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Responsibilities:
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- Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol servers) that drive measurable return on investment.
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- Architect and code the connective tissue between Google's AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
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- Build high-quality, production-grade solutions, providing white-glove deployment and acting as a feedback loop into Google Cloud's product roadmap.
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- Lead technical discovery with business stakeholders and engineering teams to define AI and infrastructure requirements.
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- Optimize LLM-native metrics (tokens/sec, cost-per-request), state management, and granular tracing.
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@@ -0,0 +1,74 @@
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# Session: Google — Senior Forward Deployed Engineer, GenAI, Google Cloud
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## JD Info
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- **File:** JDs/temp_google_fde_genai.txt
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- **Role:** Senior Forward Deployed Engineer, GenAI, Google Cloud
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- **Company:** Google (Google Cloud — Go-To-Market / Worldwide Field Operations)
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- **Bundle:** ML/AI Engineer (primary) + Data Engineer (secondary, for the pipeline/RAG-data angle)
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- **Format:** Resume (2-page, resume.cls) + 1-page cover letter
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- **Locations:** Zürich (also Vienna, Berlin, Hamburg, Munich) — German-speaking region, NO French requirement
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- **Comp context:** FDE GenAI bands anchor near top of market (~$238k avg US, into high $400k senior). Clears the 180k+ CHF bar easily.
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## JD Analysis
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### Requirements
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| # | Requirement | Match | Evidence |
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|---|-------------|-------|----------|
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| 1 | Bachelor's CS/Eng or equivalent | Direct | M.Eng. Computer Aided Engineering (Software Design & Eng focus) |
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| 2 | 6 yrs shipping production AI-driven solutions (Python/TS) | Bridge (MED) | Production ML inference at Bosch (24/7 fab); 6+ yr Python career; but "AI-driven solutions" for full 6 yrs is a stretch — GenAI specifically is recent |
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| 3 | Lead technical discovery w/ business stakeholders | Bridge (MED-HIGH) | Swisscom B2B data products, Product Owner collaboration, Component Owner, stakeholder analytics |
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| 4 | Design/build AI systems on **GCP** | Gap/Bridge (LOW) | AWS-certified & AWS-native (S3/Glue/Athena/Redshift); GCP not used. Bridge: "cloud-native AI infra, transferable to GCP" |
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| 5 | Pipelines for structured/unstructured data + **vector DBs + RAG** | Split | Pipelines: Bridge-HIGH (ETL, Kafka, AWS, PySpark). Vector DBs + RAG: **GAP — not documented** |
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| 6 | MSc/PhD (preferred) | Direct | M.Eng. |
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| 7 | Multi-agent frameworks (LangGraph/CrewAI/ADK), ReAct (preferred) | **GAP** | Verified toolchain is Kiro/Copilot/LiteLLM/custom GPTs — NOT these. DO NOT fabricate. |
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| 8 | LLM-native metrics: tokens/sec, cost-per-request (preferred) | Bridge (LOW-MED) | LiteLLM gateway work touches API usage/cost — but not documented as a quantified achievement |
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### ATS Keywords
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- **GenAI/AI:** GenAI, LLM, agentic, RAG, vector database, multi-agent, production AI, inference
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- **Cloud:** GCP, Vertex AI, Gemini, cloud-native (his: AWS — bridge)
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- **Data:** pipelines, structured/unstructured data, ETL, Kafka, data readiness
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- **Eng:** Python, TypeScript, APIs, production-grade, MLOps, Kubernetes, CI/CD
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- **Soft:** technical discovery, stakeholder, customer-facing, white-glove deployment, ROI
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### Gap Assessment
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- **Direct:** Education (M.Eng.); Python; production deployment lifecycle; stakeholder/customer-facing data delivery; data pipelines (structured); Kubernetes/CI/CD; AWS cloud-native infra
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- **Bridge:** "production AI-driven solutions" (via Bosch ML inference + MLOps); GCP (via AWS, transferable); unstructured-data pipelines; LLM cost/usage (via LiteLLM gateway — verified tooling)
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- **GAP (cannot claim):** RAG architectures, vector databases, multi-agent frameworks (LangGraph/CrewAI/ADK), agentic workflow production systems, Vertex AI/Gemini hands-on. **The JD's GenAI core is largely undocumented in the KB.**
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## Company Context
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- **Mission:** Google Cloud's Go-To-Market team leading enterprise AI adoption with Gemini + Gemini Enterprise Agent Platform (formerly Vertex AI), ADK, RAG Engine, Model Garden.
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- **This role:** Embedded builder-consultant living inside customer offices, shipping production AI code. Google is hiring *hundreds* of FDEs in 2026. Concentrated in finance/healthcare/retail/public-sector verticals where AI is **bottlenecked by compliance, data residency, and legacy-system integration — not model capability.**
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- **Culture:** Hands-on builder (code+debug, not just architecture); white-glove customer delivery; feedback loop into product roadmap; collaborative, direct access to DeepMind eng.
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- **"Why them" angle:** The bottleneck Google names — compliance, data residency, legacy integration — is *exactly* the regulated-enterprise environment Dennis works in daily at Swisscom (Switzerland's largest telco): data governance, on-call SLA, security ownership, legacy-to-cloud migration. That operational/compliance maturity is the genuine edge, not GenAI-framework depth.
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## Framing Strategy
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- **Lead narrative:** "Engineer who ships production AI into demanding, regulated, real-world environments — and owns the data infrastructure and integration work that makes it actually run." Bosch (production ML inference in a 24/7 fab) + Swisscom (regulated-telco data infra, cloud migration, compliance ownership) = the FDE archetype: deploys into hard environments, solves integration/data-readiness/compliance blockers.
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- **Reframing map:**
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- Bosch ML inference containerization → "shipped production AI into a 24/7 environment with zero downtime tolerance" (the white-glove/production-blocker story)
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- AWS data lake / ETL → "data-readiness and pipeline foundation that enterprise AI depends on; cloud-native, transferable to GCP"
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- Component Owner + on-call + Data Governance → "solved the compliance, data-residency, and reliability blockers that stall enterprise AI" (maps to Google's stated FDE bottleneck)
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- B2B data products + Product Owner collaboration → "technical discovery with business stakeholders"
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- LiteLLM gateway + custom GPTs (VERIFIED tooling) → modest, honest GenAI signal: "built internal LLM-gateway/API tooling and domain-specific GPT assistants" — NO inflation into RAG/agentic production
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- **Emphasize:** production deployment in hard environments; data-readiness/integration/compliance; stakeholder-facing delivery; Python; cloud-native infra; verified GenAI tooling
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- **Downplay:** pure BI/analytics; testing/RPA; do not lead with "data engineer" label
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- **Do NOT claim:** RAG, vector DBs, LangGraph/CrewAI/ADK, multi-agent production systems, Vertex AI/Gemini hands-on
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- **CL hooks:** Google's "hundreds of FDEs in 2026" embedded model; the compliance/data-residency/legacy-integration bottleneck (Swisscom parallel); Bosch production-ML story as the opener
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- **User directives:** No French/Italian on resume. No "Security Champion = 3 years" (it's a 2025/26 team role, not award; include only if useful — JD doesn't require security). Don't oversell C++.
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## Critique Context
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- **Reviewer persona:** Google Cloud FDE hiring manager / staff FDE — wants someone who can *code and ship* agentic GenAI in a customer's messy environment, not a slide-deck architect. Impressed by: production-grade delivery under constraints, real integration war-stories, ROI. Bored by: generic "passionate about AI," BI dashboards, untethered tool lists.
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- **Competitive landscape:** Competing against ex-consulting solutions engineers, AWS/GCP applied-AI specialists, and ML engineers with real RAG/agentic production reps. The "obvious fit" has shipped a RAG/agentic system on a hyperscaler. Dennis's edge must be: production-ML-in-hard-environments + regulated-enterprise data/compliance maturity.
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- **Domain vocabulary:** Vertex AI / Gemini Enterprise, ADK, RAG Engine, agentic workflows, MCP servers, tokens/sec, cost-per-request, data residency, VPC Service Controls — insider terms Dennis can *reference* but must not *claim hands-on* beyond what's true.
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## Cover Letter Plan
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- **Institution type:** Industry (Big Tech, applied AI field team)
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- **Paragraph count:** 3-4 paragraphs, ~250-300 words
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- **P1 hook:** Bosch production-ML story (shipped ML inference into a 24/7 fab, zero downtime) → "that's what 'production-grade reality' means to me" → tie to FDE embedded-builder mission
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- **P2-P3 evidence:** Swisscom regulated-enterprise data infra + cloud migration + compliance/SLA ownership → map directly to Google's stated FDE bottleneck (compliance, data residency, legacy integration). Mention verified GenAI tooling (LiteLLM gateway, custom GPTs) honestly as current applied-LLM work.
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- **Domain pivot:** "My GenAI work to date is internal tooling (LLM gateway, domain GPT assistants); what I bring to an FDE role is the harder-to-teach half — shipping AI into constrained, regulated, legacy-bound environments."
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- **Jargon level:** Technical (engineer-to-engineer), HR-safe on specifics
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- **"Why them" hook:** Google's 2026 embedded-FDE bet + the compliance/integration bottleneck that mirrors his daily work
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## Status
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- Phase 0: DONE
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- Phase 1: PENDING
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- **Next:** (awaiting Phase 0 confirmation — see strategic flag on GenAI evidence gap)
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@@ -0,0 +1,31 @@
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Senior Forward Deployed Engineer, GenAI, Google Cloud
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Google — Go-To-Market team, Google Cloud
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Locations: Vienna, Austria; Zürich, Switzerland; Berlin, Germany; Hamburg, Germany; Munich, Germany
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Level: Mid
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URL: https://www.google.com/about/careers/applications/jobs/results/98998917743420102-senior-forward-deployed-engineer-genai-google-cloud
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Minimum qualifications:
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- Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
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- 6 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, Typescript or comparable languages.
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- Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
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- Experience designing and building AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
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- Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise-grade AI solutions.
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Preferred qualifications:
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- Master's degree or PhD in AI, Computer Science, or a related technical field.
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- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google's Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
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- Knowledge of large language model native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
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About the job:
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As a GenAI Forward Deployed Engineer at Google Cloud, you will be an embedded builder bridging the gap between frontier AI products and production-grade reality for our customers. You will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer's environment.
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In this role, you will manage blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud's future product roadmap.
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It's an exciting time to join Google Cloud's Go-To-Market team, leading the AI revolution for businesses worldwide. We'll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform. We're a collaborative culture providing direct access to DeepMind's engineering and research minds.
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Responsibilities:
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- Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol servers) that drive measurable return on investment.
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- Architect and code the connective tissue between Google's AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
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- Build high-quality, production-grade solutions, providing white-glove deployment and acting as a feedback loop into Google Cloud's product roadmap.
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- Lead technical discovery with business stakeholders and engineering teams to define AI and infrastructure requirements.
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- Optimize LLM-native metrics (tokens/sec, cost-per-request), state management, and granular tracing.
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@@ -143,6 +143,29 @@
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---
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### Achievement SW-7: Data Mesh, Data Products & Metadata Management (AWS) — Foundation for Agentic AI
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**Source:** User-verified current work (2026), thiessen_cv_master_profile.md (AWS stack)
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**User's role:** Primary developer / current Staff-level focus area
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**Status:** Active / ongoing (current emphasis)
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**Context:** Current Staff-level work building decentralized **Data Mesh** architecture, reusable **data products**, and active **metadata management** on AWS (Glue, Athena, CloudFormation, AWS CLI, CI/CD deployments). This is the governed, discoverable data foundation that downstream AI and **agentic workflows** depend on — enabling "AI speak-to-data" / grounded retrieval over enterprise data. Directly maps to agentic reference-architecture and MCP-based tool/data-access requirements.
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**Bullet variants:**
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- **2L:** Built decentralized Data Mesh and reusable data products with active metadata management on AWS (Glue, Athena, CloudFormation, CI/CD) — the governed, discoverable data foundation that downstream AI and agentic workflows query directly.
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- **3L:** Architected decentralized Data Mesh with reusable, governed data products and active metadata management on AWS (Glue, Athena, CloudFormation, AWS CLI, automated CI/CD) — establishing the discoverable, well-described data foundation that downstream AI and agentic workflows depend on for grounded, "speak-to-data" retrieval over enterprise sources.
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- **1L:** Built AWS Data Mesh, data products and metadata management — the governed data foundation for downstream AI/agentic workflows.
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**Key skills:** Data Mesh, data products, metadata management, data catalog, data governance, AWS, Glue, Athena, CloudFormation, AWS CLI, CI/CD, agentic data foundation, grounded retrieval
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**ATS keywords:** Data Mesh, data products, metadata management, AWS, Glue, Athena, CloudFormation, CI/CD, data governance, grounded retrieval, agentic AI foundation
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**Reframing notes:**
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- ML/AI (agentic): HIGH — lead bridge to "reference architecture for agentic systems" + "grounded retrieval / MCP tool-data access"; frame data layer as what agents query
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- Data Platform/Infra: HIGH — Data Mesh + metadata + AWS IaC is core platform signal
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- Staff/Senior DE: HIGH — decentralized architecture ownership at scale
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- Analytics Engineer: MED — data products enable self-serve analytics
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---
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## Position Summary
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| Achievement | ID | Priority for DE | Priority for Analytics | Priority for ML/AI | Priority for Platform |
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@@ -153,3 +176,4 @@
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| B2B Data Products + Automation | SW-4 | MED | HIGH | MED | MED |
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| Security Champion | SW-5 | MED | LOW | MED | HIGH |
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| PySpark | SW-6 | MED | LOW | MED | MED |
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| Data Mesh / Data Products / Metadata (agentic foundation) | SW-7 | HIGH | MED | HIGH | HIGH |
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Block a user