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# Critique: Kraken — Senior Software Engineer, AI Infrastructure (Pass 2)
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**Resume File:** `output/Kraken_AI_Infrastructure/e2e_kraken_ai_infra_resume.tex`
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**CL File:** `output/Kraken_AI_Infrastructure/e2e_kraken_ai_infra_cover_letter.tex`
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**Date:** 2026-05-01
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**Pass:** 2 (Pass 1 = 81.5/100; Pass 2 trajectory below)
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---
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## Changes Since Pass 1
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All three Pass 1 Tier 1 fixes are applied and verified in the compiled PDF:
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| # | Fix | Pass 1 → Pass 2 | Verified |
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|---|-----|-----------------|----------|
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| 1 | Summary now carries crypto/Solidity hook ("Solidity smart-contract developer (personal projects); long-time Kraken customer.") | Mirrors CL opener; visible at recruiter-glance speed | ✓ resume line 47 |
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| 2 | B3 reframed with agent vocabulary: "LiteLLM-routed agent assistants (LLM API gateway, model routing)" | JD's #3 keyword now lives in a body bullet, not just a skills header | ✓ resume line 101 |
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| 3 | B6 reframed: "delivered reliable data products to downstream ML and analytics consumers" (was: B2B stakeholders / dashboards) | Removes analytics-engineer signal that diluted AI-infra story | ✓ resume line 104 |
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Char counts confirmed in budget (B3 = 208 NEAR MAX, B4 = 212 NEAR MAX, all others OK). Both documents compile clean: resume 2 pages, CL 1 page (~285 words). AI fingerprint scan: clean (em-dashes 1 + 2, no banned vocabulary, no -ing endings).
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---
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## Domain-Specialist Lens
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**Reused from Pass 1 — JD and company unchanged.** Persona, company context, JD vocabulary extraction, and competitive landscape are unchanged. Two of the four "Domain Vocabulary Map" rows from Pass 1 are now closed (B3 agent reframe + summary crypto signal).
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### Updated Vocabulary Map (post-fix delta only)
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| Pass 1 finding | Pass 2 status |
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|----------------|---------------|
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| B3 missing "agent" framing | ✓ CLOSED — "agent assistants" now in B3 |
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| Summary missing crypto/Solidity | ✓ CLOSED — last clause of summary |
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| B6 "B2B dashboards" diluting AI-infra | ✓ CLOSED — reframed to ML/analytics consumers |
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| LiteLLM under-signalled as agent infra | PARTIAL — bullet now says "LLM API gateway, model routing"; skills group still says "LiteLLM (LLM API gateway)" only — could add "/ agent routing" |
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### Gap Ranking (updated)
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- **Fatal:** None.
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- **Serious:** Rust production absence — unchanged, structural. Hard ceiling stays ~88.
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- **Cosmetic:** Tokio specifically, "guardrails" exact term, MCP server experience.
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---
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## Five-Perspective Read-Through (delta)
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### ATS Robot
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**Match rate:** ~80% (was 76%). New body-bullet hits: "agent assistants" (B3), "ML and analytics consumers" (B6 — adds the soft ML signal where the dashboard line was).
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| JD Keyword | Pass 1 | Pass 2 |
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|------------|--------|--------|
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| AI agents / agent systems | PARTIAL (skills header only) | YES (B3 + skills) |
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| failure recovery | PARTIAL (on-call only) | PARTIAL (unchanged) |
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| Rust | NO | NO (structural) |
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| guardrails | NO | NO |
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| execution layer | NO | NO (CL has it) |
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Three high-value JD terms still absent in resume body: Rust, guardrails, execution layer. Only one of these (guardrails) is bridgeable truthfully; Rust and execution layer are structural.
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### Recruiter Glance (10s)
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**Verdict: FORWARD (stronger).** Summary's last clause now telegraphs the Kraken-specific differentiator within the recruiter's 10-second window. "Solidity smart-contract developer; long-time Kraken customer" is the single line that separates Dennis from a generic ML infra applicant — and it's now visible without scrolling to skills group #5.
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### HR Screen (30s)
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**Verdict: PHONE SCREEN (unchanged).**
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### Hiring Manager (2m)
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**Verdict: INTERVIEW (firmer than Pass 1).**
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**Top 3 things HM notices now:**
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1. **BS-1 + BS-4 are still gold** — production ML inference in 24/7 fab + the exact Kraken-described observability stack. Unchanged.
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2. **Crypto signal lands in summary** — HM no longer has to dig to find the "long-time Kraken customer" beat that the JD explicitly invites. Pairs naturally with Solidity in skills group #5.
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3. **B3 "agent assistants" reads as honest production analog** — HM sees real LLM-gateway / routing work without inflation. The phrase "LLM API gateway, model routing" is the technical handshake.
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**Predicted first interview question (unchanged):** *"Walk me through what 'no maintenance windows' actually meant at Bosch — what was your blast radius if a bad model version shipped?"*
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### Technical Reviewer (10m)
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**Truthfulness, verb discipline, internal consistency: all clean (rechecked).** No new claims introduced; no fabrications; LangChain still absent; FC-2 still hedged ("Contributed"). Em-dash count: resume 1, CL 2 — under limit.
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---
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## Eight-Dimension Scoring (Pass 2)
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| # | Dimension | Pass 1 | Pass 2 | Weight | Weighted | Notes |
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|---|-----------|--------|--------|--------|----------|-------|
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| 1 | ATS Keywords | 8.0 | **8.3** | 15% | 1.245 | Agent now in body; Rust + guardrails still absent |
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| 2 | Summary | 8.0 | **8.7** | 10% | 0.870 | Crypto/Solidity hook lands in last clause; bridge sentence still strong |
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| 3 | Skills Section | 8.5 | 8.5 | 10% | 0.850 | Unchanged — Crypto/Web3 group still a Kraken-specific power move |
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| 4 | Bullet Quality | 8.0 | **8.5** | 25% | 2.125 | B3 agent reframe + B6 dilution removed; BS-1 + BS-4 + VZ-1 still load-bearing |
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| 5 | Publications | 8.0 | 8.0 | 10% | 0.800 | No pubs section — appropriate |
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| 6 | Narrative Coherence | 8.0 | **8.5** | 15% | 1.275 | Crypto thread now arcs header tagline → summary → skills → CL (was floating) |
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| 7 | Page Fill & Visual | 9.0 | 9.0 | 5% | 0.450 | 2 pages, no orphans, page 2 reaches Languages line |
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| 8 | Credibility Signals | 8.5 | 8.5 | 10% | 0.850 | Unchanged |
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| **Total** | | **81.5** | | **100%** | **8.465** | **= 84.5/100** |
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**Trajectory:** Pass 1 = 81.5 → Pass 2 = 84.5 (+3.0). Matches Pass 1's projection ("+ Tier 1 fixes applied: 84.5").
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---
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## Interview Likelihood (updated)
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| Reader | Pass 1 | Pass 2 | Key Factor |
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|--------|--------|--------|------------|
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| ATS | ~75% | **~80%** | "agent" now appears in bullets; Rust still missing |
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| Recruiter (10s) | ~85% | **~88%** | Crypto signal visible in summary closer |
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| HR (30s) | ~80% | ~80% | Unchanged — strong bridge sentence |
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| Hiring Manager (2m) | ~55-65% | **~65-70%** | Three Pass 1 friction points closed; Rust gap remains |
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| Technical Panel (10m) | ~50% strong yes | ~55% strong yes | Production ML + observability stack are real; Rust gap surfaces here |
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**Ceiling Analysis:**
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| Scenario | Score |
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|----------|-------|
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| Pass 1 (pre-fix) | 81.5 |
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| Pass 2 (Tier 1 applied — current) | **84.5** |
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| Theoretical max (this candidate, this JD) | ~86 |
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| Hard ceiling (Rust production gap) | ~88 |
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| Closes the gap | 6+ months Rust production OR public Rust project (Foundry/Anchor adjacent) |
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**Verdict on score motion:** Pass 2 is within ~1.5 points of theoretical max. Score has effectively stopped moving — declaring Pass 2 the ceiling for this candidate-JD pairing. Tier 2 polish below would add ~0.3-0.6 points each at diminishing return.
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---
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## Actionable Improvements (Pass 2)
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### Tier 1: NONE remaining
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All Pass 1 Tier 1 fixes were applied. No new Tier 1 issues surfaced.
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### Tier 2 (MEDIUM — optional polish, ~0.3-0.6 each)
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1. **Skills group #1 — add "agent orchestration" / "guardrails":** Current line ends "...evaluation frameworks, computer vision, NLP". Suggested: "...evaluation frameworks, **agent orchestration**, **guardrails**, computer vision, NLP" — direct JD vocabulary lift, honest at the skills-familiarity level (LiteLLM/custom GPTs work touches both).
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2. **B4 (SW-3 K8s) trim 212 → ~205 chars:** "Deployed and operate **Python** data services on **Kubernetes** with GitLab CI/CD, owning containerized delivery from build and test to production rollout across multiple data products in an agile DevOps team." (-7 chars; same content). Removes the NEAR MAX flag.
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3. **CL closing — add active bridge:** Current passive close. Suggested addition before signature: "Happy to walk through how the Bosch fab MLOps pattern would map to model-serving and agent execution at Kraken." Converts a passive Krakenite line into an interview opener.
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4. **Generali subsection — reorder bullets:** Lead with Java/J2EE backend (currently last), drop or move BDD lead. Java backend is more relevant to Kraken than BDD test automation. Reorder: GN-3 → GN-1 → GN-2 (or omit GN-2). Worth ~0.2 — borderline Tier 2/3.
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5. **Skills group #1 — slight LiteLLM edit:** Add "/ agent routing" parenthetical: "Custom GPTs, **LiteLLM** (LLM API gateway / agent routing), **Kiro** / spec-driven dev..." — makes the agent-infra signal louder where ATS scans.
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### Tier 3 (COSMETIC — skip)
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- Generali subsection title rename
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- B8 borderline -ing ending (concrete enough to leave alone)
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### Verdict
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**Score has effectively converged.** Tier 2 #1 (skills "agent orchestration / guardrails") and Tier 2 #3 (CL active bridge) are the only edits that might add real signal — both ~0.3-0.5 points. Submit-ready as-is. Recommendation: ship Pass 2 unless you want a polish round; if you do, only #1 and #3 are worth the edit.
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---
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## Interview Bridge Points (unchanged from Pass 1)
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| Resume Topic | Kraken Equivalent | Opening Line |
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|--------------|-------------------|--------------|
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| Bosch BS-1 24/7 ML inference | Model inference + agent execution at p99 latency | "The same operational shape — uptime non-negotiable, no maintenance windows, every observability gap is a yield problem — is what shapes how I'd think about agent inference at Kraken." |
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| Bosch BS-4 ELK + Kafka + Grafana + Prometheus + Loki | The observability pattern Oxidizing Kraken describes | "I've already run the same stack pattern Kraken describes for keeping high-throughput async services honest — just on a fab, not an exchange." |
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| Swisscom SW-1 AWS migration with CFN IaC | Cloud-native infra credibility | "The pattern is the same: declarative IaC, replicable environments, observability built in from day one — what changes is the workload class." |
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| Swisscom SW-2 Component Owner on-call SLA | Reliability engineering ownership at scale | "I already carry production accountability — being woken up at 3am for a Component Owner pager is the SLA." |
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| Swisscom B3 LiteLLM + custom GPTs (agent assistants) | Agent-style LLM gateway / routing | "LiteLLM as a routing layer is small-scale agent infrastructure — same primitives Kraken needs, just at lower throughput than yours." |
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| Vizrt VZ-1 distributed real-time A/V transcoding | Distributed systems + low-latency credibility | "Real-time A/V transcoding for CNN/BBC/Al Jazeera is the systems-level production work behind the C++ background — the discipline transfers to Rust." |
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| Solidity + Kraken since 2017 | Crypto-native engineering interest | "I write Solidity in my free time and have been a Kraken customer since 2017 — coming to this team as a long-time user, not a tourist." |
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---
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## Cover Letter Critique (Pass 2 — unchanged from Pass 1)
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CL was not edited between passes; all 6A-6F checks pass as in Pass 1. Word count ~285 (Industry 250-300 target ✓). Em-dash count = 2 (limit). All Kraken hooks verified (Oxidizing Kraken via blog.kraken.com, Kraken CLI via github.com/krakenfx/kraken-cli, Solidity + Kraken-since-2017 from user_crypto.md memory). The one Pass 1 Tier 2 suggestion (active-bridge closer) remains optional and unapplied.
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### 6F. Package Cohesion (re-checked)
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- ✓ Resume earns interview standalone (Pass 2 score 84.5 alone is interview-strength).
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- ✓ Resume summary now echoes the CL's strongest hook — Pass 1 ⚠️ resolved.
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- ✓ No date/metric/framing contradictions across documents.
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- ✓ CL deepens (operational shape, methodology transfer, Rust honesty paragraph) without introducing new claims.
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### 6G. AI Fingerprint Scan
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- Em-dashes: Resume 1, CL 2 — at limit ✓
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- No Tier 1 banned words ✓
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- No -ing analysis bullet endings (B2, B8 borderline but end with concrete nouns) ✓
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- CL paragraph openers vary (`I have been...`, `My most defining...`, `At Swisscom...`, `On Rust...`, `I am based...`) ✓
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- Sentence length variety in CL (10-word and 30-word sentences mixed) ✓
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**Clean.**
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---
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## Part 7: Post-Generation Verification
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### Mechanical
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- [x] All bullets within char limits (B3 = 208, B4 = 212 — NEAR MAX, in range; all others OK)
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- [x] Page fill: 2/2 pages, page 2 reaches Languages line cleanly — well-filled, no orphans
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- [x] No ordering errors
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### Content
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- [x] ATS keyword match ~80% (was 76% in Pass 1) — PASS
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- [x] All provenance flags correct
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- [x] No forbidden terms (LangChain ✓, no Capgemini ✓, no inflated Security Champion ✓)
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- [x] No LOC counts, no test counts ✓
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- [x] No code folder names as packages (ARTUS, MISSION, SCEDAS, PIA-Postkorb properly described) ✓
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- [x] Email matches config.md (`dennis@thiessen.io`) ✓
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- [x] No fabricated tools — all GenAI tools (Kiro, LiteLLM, custom GPTs, Copilot) verified
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- [x] CL claims traceable to resume bullets (Oxidizing Kraken / Kraken CLI verified)
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### Structural
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- [x] Company name spelled correctly (Kraken, Payward Inc.)
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- [x] .tex compiles standalone (verified — 2pp resume + 1pp CL)
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- [x] Date format consistent
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- [x] Page count: resume 2, CL 1 ✓
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**All Part 7 checks pass.**
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---
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*Pass 2 complete. Score: 84.5/100 — converged near theoretical max (~86). Hard ceiling ~88 (Rust gap). Submit-ready.*
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---
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# Pass 1 Critique (preserved for trajectory)
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> **Score:** 81.5/100 — see Pass 2 above for current state.
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[Pass 1 lens, five-perspective read-through, scoring, and bridge points preserved by reference. Key Pass 1 findings closed in Pass 2: (1) summary missing crypto signal — CLOSED; (2) B3 missing agent vocab — CLOSED; (3) B6 dashboards dilution — CLOSED. Pass 1 file content collapsed; reconstructable from session file Critique Summary section if needed.]
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\documentclass[11pt,a4paper,roman]{moderncv}
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\usepackage[english]{babel}
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\moderncvstyle{classic}
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\moderncvcolor{green}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{lmodern}
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\usepackage{ragged2e}
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\usepackage[scale=0.79]{geometry}
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\usepackage[version=4,arrows=pgf-filled]{mhchem}
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\renewcommand*{\makeletterclosing}{\par\vspace{2ex}\closingname\par}
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\name{Dennis}{Thiessen}
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\address{Bern, Switzerland}
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\phone[mobile]{+41~795~955~585}
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\email{dennis@thiessen.io}
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\begin{document}
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\recipient{To}{Kraken AI Infrastructure Team\\Payward Inc.\\Remote (Switzerland)}
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\date{\today}
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\opening{Dear Kraken AI Infrastructure Team,}
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\makelettertitle
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\begin{justify}
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I have been a Kraken customer since 2017, and in my free time I write Solidity smart contracts. So when I read the Senior Software Engineer, AI Infrastructure posting, the work itself is what I would want to do regardless of who was hiring: an agent-first execution layer at a crypto exchange where Rust took over the backend after Oxidizing Kraken, and where Kraken CLI just shipped as MCP-native infrastructure for Claude, Cursor, and Codex.
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My most defining ML deployment was at Bosch Semiconductor in Dresden, where I designed and shipped the inference infrastructure (Docker, Kubernetes, Ansible) into a 24/7 wafer fab. Image classification ran continuously against production data, with no maintenance windows and hardware-in-the-loop constraints. That operational shape, where uptime is non-negotiable and every observability gap is a yield problem, is what I would carry into model-serving and agent infrastructure at Kraken.
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At Swisscom, Switzerland's largest telco, I currently own Kubernetes-deployed Python data services on AWS (S3, Glue, Athena/Iceberg, Redshift, Airflow with CloudFormation IaC), Kafka-based streaming, and the on-call SLA that comes with Component Owner accountability. At Bosch I introduced the observability stack --- ELK with Kafka, Grafana, Prometheus, Loki --- the same pattern Oxidizing Kraken describes for keeping high-throughput async systems honest. I have also built custom GPTs and LiteLLM-routed LLM API integrations on a spec-driven Kiro toolchain to automate engineering workflows.
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On Rust: my systems-level production background is C++ (Vizrt distributed video transcoding for CNN, BBC, Al Jazeera) and Python at scale. I am building Rust depth currently and not claiming production years I do not have.
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I am based in Bern and remote-eligible for Switzerland. Long-time Krakenite as a customer; I would be glad to be one as an engineer.
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\end{justify}
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\vspace{0.3cm}
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{Sincerely,\\
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Dennis Thiessen, M.Eng.\\
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Staff Data, Analytics \& AI Engineer\\
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Swisscom (Schweiz) AG}
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\end{document}
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\documentclass{resume}
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\usepackage{hyperref}
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\usepackage{enumitem}
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\hypersetup{
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colorlinks = true,
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\usepackage{fancyhdr}
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\pagestyle{fancy}
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\fancyhf{}
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\renewcommand{\headrulewidth}{0pt}
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\fancyfoot[R]{\hfill \thepage/\pageref{LastPage}}
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\newcommand{\tab}[1]{\hspace{.2667\textwidth}\rlap{#1}}
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\newcommand{\itab}[1]{\hspace{0em}\rlap{#1}}
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%----------------------------------------------------------------------------------------
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% HEADER
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%----------------------------------------------------------------------------------------
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\name{Dennis Thiessen, M.Eng.}
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\address{\href{https://linkedin.com/in/dennis-thiessen}{LinkedIn}}
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\address{dennis@thiessen.io \\ +41 795 955 585}
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\address{Bern, Switzerland $\vert$ German citizen $\vert$ Available remote across DACH/EU/UK}
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\address{{AI Infrastructure Engineer $\vert$ Model Inference $\cdot$ MLOps $\cdot$ Observability $\vert$ K8s $\cdot$ AWS $\cdot$ Python}}
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\begin{document}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% SUMMARY
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Summary}
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Software engineer with 11+ years building production data and AI infrastructure --- containerized \textbf{ML inference} into a 24/7 Bosch semiconductor fab (\textbf{Docker}, \textbf{Kubernetes}, Ansible), and currently own Switzerland's largest telco's cloud-native data platform on \textbf{AWS} (\textbf{Airflow}, Kafka, PySpark, GitLab CI/CD). Built \textbf{custom GPTs} and \textbf{LiteLLM}-routed agent assistants to automate engineering workflows. Earlier engineered distributed real-time backends at Vizrt for CNN, BBC, Al Jazeera. \textbf{Python} expert; AWS Solutions Architect; \textbf{Solidity} smart-contract developer (personal projects); long-time Kraken customer.
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\end{rSection}
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\vspace{-0.15cm}
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%----------------------------------------------------------------------------------------
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% TECHNICAL SKILLS — Format C, 5 groups
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%----------------------------------------------------------------------------------------
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\begin{rSection}{Technical Skills}
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\begin{skillgroup}{AI / ML Infrastructure \& Agentic Workflows}
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\skilldash{\textbf{ML inference}, \textbf{model serving}, \textbf{MLOps}, model deployment, evaluation frameworks, computer vision, NLP}
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\skilldash{\textbf{Custom GPTs}, \textbf{LiteLLM} (LLM API gateway), \textbf{Kiro} / spec-driven dev, GitHub Copilot, prompt engineering}
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\skilldash{\textbf{PyTorch}, Scikit-learn, TensorFlow/Keras, Spark ML, deep learning, time-series analysis, anomaly detection}
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\skilldash{Speech recognition, image classification, defect detection, predictive maintenance, multi-modal data processing}
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\skilldash{ML dataset curation, data quality validation, model performance monitoring, observability for ML systems}
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\end{skillgroup}
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||||
|
||||
\begin{skillgroup}{Distributed Systems \& Data Engineering}
|
||||
\skilldash{\textbf{Kafka}, \textbf{Airflow}, \textbf{PySpark} / Apache Spark, Apache Iceberg, Hadoop / ImpalaSQL, Databricks}
|
||||
\skilldash{\textbf{AWS} (S3, Glue, Athena/Iceberg, Redshift, Lambda, \textbf{Airflow}, CloudFormation), Teradata, OracleDB}
|
||||
\skilldash{ETL/ELT pipeline design, data modeling, data governance, SLA / on-call ownership, batch and stream processing}
|
||||
\skilldash{High-throughput data pipelines, real-time event processing, data lakehouse, distributed batch, data lineage}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{Cloud-Native Infrastructure \& Observability}
|
||||
\skilldash{\textbf{Kubernetes}, \textbf{Docker}, Ansible, GitLab CI/CD, Jenkins, Infrastructure as Code, DevSecOps}
|
||||
\skilldash{ELK Stack (Elasticsearch, Logstash, Kibana), \textbf{Grafana}, \textbf{Prometheus}, Loki, log aggregation, alerting}
|
||||
\skilldash{AWS Lambda, CloudWatch, ECR, ECS, Step Functions, SQS, SNS, event-driven architectures, serverless}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{Programming Languages \& Tools}
|
||||
\skilldash{\textbf{Python} (expert), \textbf{Java} (strong), SQL, JavaScript, Bash, Git, .NET / Entity Framework, FastAPI}
|
||||
\skilldash{Pandas, NumPy, SQLAlchemy, pytest, Jupyter Notebooks, dbt, code review, Agile/Scrum, software design patterns}
|
||||
\skilldash{C++ (Vizrt 2017--18, legacy), C\# (Bosch / Fraunhofer 2018--22, legacy), Express.js, shell scripting}
|
||||
\end{skillgroup}
|
||||
|
||||
\begin{skillgroup}{Crypto / Web3 \& Certifications}
|
||||
\skilldash{\textbf{Solidity} (Ethereum smart contracts, personal projects), blockchain / DeFi, Kraken (long-term user since 2017)}
|
||||
\skilldash{AWS Certified Solutions Architect -- Associate (active until Sep 2027), Data Engineering with AWS (Udacity, 2026)}
|
||||
\skilldash{IBM AI Engineering Specialization, AI for Trading Nanodegree (Udacity, 2021), iSAQB CPSA-F (2016)}
|
||||
\end{skillgroup}
|
||||
|
||||
\end{rSection}
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% PROFESSIONAL EXPERIENCE
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection}{Professional Experience}
|
||||
|
||||
% --- Swisscom (Oct 2023 -- Present) — 5 bullets: SW-2, SW-1, SW-GenAI, SW-3, SW-6 ---
|
||||
\begin{rSubsection}{AI/ML Infrastructure, Agentic Workflows \& Cloud-Native Pipelines}{\textcolor{black!60}{Oct 2023 -- Present}}{Staff Data, Analytics \& AI Engineer, Swisscom (Schweiz) AG}{Bern, Switzerland}
|
||||
\item Owned Fulfillment and Product Analysis ETL pipelines (Oracle, \textbf{Kafka} to Teradata in \textbf{Python}) as Component Owner; enforced data governance and SLA compliance for business-critical telecom-scale production flows.
|
||||
\item Migrated legacy Teradata/Oracle ETL stack to \textbf{AWS} cloud-native (S3, Glue, \textbf{Airflow}, Athena/Iceberg, Redshift, CloudFormation IaC), enabling serverless data processing for ML and analytics workloads.
|
||||
\item Built \textbf{custom GPTs} and \textbf{LiteLLM}-routed \textbf{agent assistants} (LLM API gateway, model routing) to automate Swisscom engineering workflows (code review, documentation, pipeline triage) on a spec-driven \textbf{Kiro} toolchain.
|
||||
\item Deployed and operate \textbf{Python} data services on \textbf{Kubernetes} with GitLab CI/CD automation, owning containerized delivery from build and test to production rollout in an agile DevOps team across multiple data products.
|
||||
\item Applied \textbf{PySpark} and distributed computing within the Swisscom Data Lake platform, extending \textbf{Python} pipeline capabilities to large-scale batch workloads for Fulfillment and Product Analysis data.
|
||||
\item Drove \textbf{Python} process automation and 3rd-level root cause analysis across recurring data workflows under on-call SLA; delivered reliable data products to downstream \textbf{ML} and analytics consumers.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Bosch (Feb 2020 -- Dec 2022) — 4 bullets: BS-1, BS-4, BS-3, BS-2 ---
|
||||
\begin{rSubsection}{Production ML Inference \& Observability in 24/7 Semiconductor Manufacturing}{\textcolor{black!60}{Feb 2020 -- Dec 2022}}{(Senior) Data \& ML Engineer, Robert Bosch Semiconductor Manufacturing}{Dresden, Germany}
|
||||
\item Designed \textbf{ML inference} infrastructure (\textbf{Docker}, \textbf{Kubernetes}, Ansible) for Bosch's 24/7 semiconductor fab, automating image-based defect classification across 300mm wafer production lines without downtime.
|
||||
\item Built anomaly detection PoC: ELK Stack with \textbf{Kafka} (\textbf{Docker}), \textbf{Grafana}, \textbf{Prometheus} and Loki monitoring, providing centralized observability for 24/7 semiconductor manufacturing infrastructure.
|
||||
\item Served as Application Owner for the semiconductor analytics suite and upstream data pipelines, defining SLOs, managing vendors, and delivering user training and documentation across fab operations teams.
|
||||
\item Built data services in \textbf{Python}, Java and C\# over OracleDB and Hadoop/ImpalaSQL, supplying semiconductor analysis teams with structured access to defect management and process optimization data.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Fraunhofer (Sep 2018 -- Oct 2019) — 3 bullets: FC-2, FC-1, FC-3 ---
|
||||
\begin{rSubsection}{Applied NLP/ML Research \& Microservice Engineering}{\textcolor{black!60}{Sep 2018 -- Oct 2019}}{Research Software Engineer, Fraunhofer-Center for Maritime Logistics CML}{Hamburg, Germany}
|
||||
\item Contributed \textbf{ML and NLP} components to ARTUS, a Fraunhofer research project for automatic sea rescue transcription combining speech recognition and machine learning in a safety-critical maritime domain.
|
||||
\item Set up Jenkins CI/CD pipeline with quality gates independently, introducing build automation to the research team; developed SCEDAS crew scheduling software (C\#, .NET, MS SQL Server, Entity Framework).
|
||||
\item Built microservices (Express.js, \textbf{Docker}, SQLite) for MISSION, a Fraunhofer research platform for maritime data exchange across logistics stakeholders, ports, operators and research partners.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Vizrt (Jul 2017 -- May 2018) — 2 bullets: VZ-1 (Python-led), 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 Built 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 Built automated integration and unit test suite for A/V streaming (\textbf{Python}) and integrated quality gates into CI/CD, which shortened the feedback loop for new features and raised release quality.
|
||||
\end{rSubsection}
|
||||
|
||||
% --- Generali (May 2015 -- Jun 2017) — 2 bullets: GN-1, GN-3 ---
|
||||
\begin{rSubsection}{Test Automation, BDD 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), running the initial PoC and taking technical ownership; trained teams and presented the methodology to the Java Community.
|
||||
\item Pioneered UIPath RPA at Generali GDIS, developing PoCs and serving as internal RPA contact for Generali group companies; extended automation from test tooling into business process automation.
|
||||
\item Developed Java/J2EE application features for the PIA-Postkorb workflow portal; migrated WebServices to the XLDeploy process and contributed to an Apache Camel / Spring Boot dispatcher integration PoC.
|
||||
\end{rSubsection}
|
||||
|
||||
|
||||
\end{rSection}
|
||||
\vspace{-0.15cm}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% EDUCATION — FIXED
|
||||
%----------------------------------------------------------------------------------------
|
||||
\begin{rSection}{Education}
|
||||
{M.Eng.\ Computer Aided Engineering (Software Design \& Engineering)} \hfill {\textcolor{black!60}{Oct 2010 -- Jul 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 2007 -- Sep 2010}}\\
|
||||
{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{IBM AI Engineering Specialization}, Coursera. Deep learning, TensorFlow, Keras, Apache Spark ML.
|
||||
\item \textbf{AI for Trading Nanodegree}, Udacity / WorldQuant (2021). Quantitative ML, time-series analysis.
|
||||
\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).
|
||||
\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 +1,216 @@
|
||||
# Session: Kraken — Senior Software Engineer, AI Infrastructure
|
||||
|
||||
**Status:** Phase 0: DONE — awaiting user confirmation before Phase 1
|
||||
**Created:** 2026-05-01
|
||||
**JD source:** `JDs/Senior Software Engineer – AI Infrastructure @ Kraken.pdf`
|
||||
**Output folder:** `output/Kraken_AI_Infrastructure/`
|
||||
|
||||
---
|
||||
|
||||
## JD Info
|
||||
|
||||
| Field | Value |
|
||||
|-------|-------|
|
||||
| Company | Kraken (Payward Inc.) — global crypto exchange |
|
||||
| Role | Senior Software Engineer — AI Infrastructure |
|
||||
| Department | Engineering, AI & Machine Learning |
|
||||
| Location | Remote — Switzerland eligible (Dennis lives in Bern → DIRECT match) |
|
||||
| Format | 2-page resume + 1-page CL |
|
||||
| Bundle (primary) | ML / AI Engineer |
|
||||
| Bundle (secondary) | Data Platform / Infra |
|
||||
|
||||
---
|
||||
|
||||
## Requirements Table
|
||||
|
||||
| # | Requirement | Status | Evidence / Bridge |
|
||||
|---|-------------|--------|-------------------|
|
||||
| 1 | 5+ yrs building/operating high-scale production systems | DIRECT | 11+ yrs (Swisscom + Bosch + Vizrt + Fraunhofer + Generali) |
|
||||
| 2 | Strong proficiency in **Rust** and systems-level programming | **GAP** (bridge LOW-MED) | C++ systems work at Vizrt (distributed video transcoding); Python/Java production. NO Rust production. |
|
||||
| 3 | Distributed systems, reliability, performance optimization | DIRECT | Vizrt distributed transcoding; Swisscom Kafka/Teradata at scale; Bosch 24/7 fab |
|
||||
| 4 | Services serving millions of users / high-throughput | DIRECT | Swisscom (Switzerland's largest telco, ~6M customers); Vizrt (CNN/BBC/Al Jazeera) |
|
||||
| 5 | ML infra / model serving / MLOps | DIRECT | Bosch BS-1: containerized ML inference in 24/7 fab; Swisscom K8s/AWS infra |
|
||||
| 6 | Observability, monitoring, failure recovery | DIRECT | Bosch BS-4: ELK + Grafana + Prometheus + Loki; on-call SLA at Swisscom |
|
||||
| 7 | Cross-team collaboration | DIRECT | Component Owner at Swisscom; App Owner at Bosch |
|
||||
| 8 | High ownership in high-stakes prod | DIRECT | 24/7 fab ML deployment; Component/App Owner roles |
|
||||
| 9 | NTH: agent/LLM-powered systems | BRIDGE (MED) | Swisscom GenAI/custom GPTs (per user memory); ARTUS NLP at Fraunhofer |
|
||||
| 10 | NTH: high-perf networking, async, low-latency | BRIDGE (MED) | Vizrt real-time A/V transcoding; Kafka streaming at Swisscom |
|
||||
| 11 | NTH: container orchestration, cloud-native | DIRECT | K8s × 2 employers; AWS migration with CloudFormation/IaC |
|
||||
| 12 | NTH: evaluation frameworks, model perf monitoring at scale | BRIDGE (MED) | Anomaly detection PoC at Bosch; observability stack |
|
||||
| 13 | NTH: 0→1 / platform-building | DIRECT | Introduced ELK observability at Bosch; introduced CI/CD at Fraunhofer/Generali |
|
||||
| 14 | NTH: crypto / blockchain | BRIDGE (HIGH) | Long-term Kraken customer since 2017 (BTC + ETH); Solidity smart-contract dev in free time; active user of Kraken / Kraken Pro / Krak apps. Genuine enthusiast — strong CL hook. |
|
||||
|
||||
**Summary:** 11 of 14 are DIRECT or DIRECT-equivalent matches. The single hard gap is **Rust production experience**. Crypto domain is an acceptable gap (Kraken invites enthusiasts).
|
||||
|
||||
---
|
||||
|
||||
## ATS Keywords (extracted from JD)
|
||||
|
||||
**Tier 1 (must appear in resume):**
|
||||
- Rust (handle carefully — see Honest Framing below)
|
||||
- ML inference, model serving, MLOps, model deployment
|
||||
- distributed systems, reliability engineering, performance optimization
|
||||
- observability, monitoring, failure recovery
|
||||
- Kubernetes, container orchestration, cloud-native
|
||||
- production systems, high-throughput, scalable systems
|
||||
- AI agents, agent systems, LLM
|
||||
- async, low-latency
|
||||
|
||||
**Tier 2 (nice to embed):**
|
||||
- Python (Dennis primary), C++ (Vizrt evidence)
|
||||
- Kafka, Airflow, Apache Iceberg, AWS
|
||||
- CI/CD, GitLab, Jenkins, Docker, Ansible
|
||||
- Prometheus, Grafana, ELK
|
||||
- DevSecOps, security compliance
|
||||
|
||||
---
|
||||
|
||||
## Gap Assessment
|
||||
|
||||
| Gap | Bridge framing | Confidence | Decision |
|
||||
|-----|---------------|-----------|----------|
|
||||
| Rust production | "Systems-level proficiency in C++ (Vizrt distributed video transcoding); building toward Rust" — list Rust ONLY in a "Learning" row, never alongside production languages | LOW-MED | Bridge honestly; do NOT inflate. Skills section must reflect this. |
|
||||
| Crypto/blockchain | Long-term Kraken customer since 2017; Solidity smart-contract dev in free time; active Kraken/Kraken Pro/Krak app user. | HIGH (genuine enthusiast) | Lead the CL with this. Optionally add a small "Crypto/Blockchain — Solidity (smart contracts), Kraken (long-term user)" line in resume Skills if space permits. |
|
||||
| Direct LLM serving infra | "Containerized ML inference in 24/7 production (Docker, K8s, Ansible)" — closest analog | MED | Use as proxy; do not claim "LLM serving experience". |
|
||||
| Trillion-row workloads / millions QPS | "Production data infrastructure at Switzerland's largest telco" — implies scale without overclaim | MED | Frame via Swisscom/Bosch fab context. |
|
||||
|
||||
---
|
||||
|
||||
## Company Context
|
||||
|
||||
**Kraken** is one of the world's largest crypto exchanges (founded 2011), now ~200+ Rust engineers and "millions of lines of Rust across hundreds of services" per their engineering blog (Oxidizing Kraken Parts 1 & 2). They've made a deliberate, multi-year bet on Rust for backend services, migrating from PHP and modernizing core infrastructure.
|
||||
|
||||
**The AI Infrastructure team** specifically powers AI agent systems in production. In Nov 2025 Kraken open-sourced **Kraken CLI** — the first crypto exchange CLI built natively for AI agents (Rust binary, MCP server compatible with Claude Code/Cursor/Codex, paper-trading engine). This team builds the inference, orchestration, and execution layers behind that.
|
||||
|
||||
**Mission:** "Accelerate the global adoption of crypto so everyone can achieve financial freedom and inclusion." Strong crypto-ethos culture — they explicitly value crypto conviction.
|
||||
|
||||
**Why this team:** Production-oriented, deeply systems-focused, building 0→1 agent infrastructure at high scale.
|
||||
|
||||
---
|
||||
|
||||
## Framing Strategy
|
||||
|
||||
**Lead narrative:** "Production AI/ML infrastructure engineer — owns model inference, container orchestration, and observability in 24/7 high-stakes environments; brings full cloud-native data platform depth from Switzerland's largest telco."
|
||||
|
||||
**Reframing map (selected):**
|
||||
- BS-1 (ML inference containerization, Bosch fab) → "Designed and deployed model inference infrastructure (Docker, Kubernetes, Ansible) into 24/7 production — image classification serving with zero-downtime constraint."
|
||||
- SW-3 (K8s + GitLab CI/CD, Swisscom) → "Architected and operate Kubernetes-deployed Python services with full GitLab CI/CD automation in agile DevOps environment."
|
||||
- SW-1 (AWS migration) → "Re-architected legacy ETL stack to cloud-native AWS infrastructure (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation IaC) — scalable, observable, serverless data layer."
|
||||
- SW-2 (Component Owner) → "Component Owner for business-critical pipelines under on-call SLA — full reliability engineering ownership at scale."
|
||||
- BS-4 (ELK/Grafana/Prometheus) → "Designed observability stack (ELK + Kafka, Grafana, Prometheus, Loki) for high-volume 24/7 production — anomaly detection and monitoring built from zero."
|
||||
- **SW-GenAI (corrected — no LangChain)** → "Built custom GPTs and LiteLLM-based LLM API integrations to automate engineering workflows (code review, documentation, pipeline triage) in a spec-driven (Kiro) development environment at Swisscom." — LiteLLM is a strong AI-infra signal (model-gateway abstraction).
|
||||
- VZ-1 (Vizrt distributed transcoding) → **Python-led** framing: "Built distributed real-time backend components in Python (with legacy C++ modules) for Vizrt's broadcast platform serving CNN, BBC, Al Jazeera at scale." C++ mentioned as legacy context only — do not lead with or bold C++.
|
||||
|
||||
**Honest framing on Rust + C/C++ (per user feedback 2026-05-01):**
|
||||
- **Rust:** DO NOT include alongside production languages. Optional: brief "Rust (active learning)" only if it doesn't crowd the line — otherwise omit; rely on systems-level / distributed-systems signal from Vizrt and bridge in CL.
|
||||
- **C/C++:** Per user feedback, do NOT lead with or bold C++. It's been many years and the user is not confident. Mention only as legacy context (e.g., "Python (with legacy C++ modules)"); if listed in skills, place last with no emphasis. Python and Java are the strong signals.
|
||||
|
||||
**GenAI / agent toolchain (CORRECTED 2026-05-01 — LangChain was a fabrication):**
|
||||
Verified tools: **Kiro** (AI IDE / Spec-Driven Development), **VS Code + Copilot**, **LiteLLM** (LLM API gateway — created/used APIs), **custom GPTs** with fed domain knowledge.
|
||||
DO NOT list LangChain, LangGraph, or LlamaIndex anywhere — they have not been used. Apple and Infineon resume outputs contain LangChain as a fabrication and need cleanup later.
|
||||
|
||||
**Emphasize:** MLOps in 24/7 production, Kubernetes ownership × 2 employers, observability stack, distributed systems, async/streaming (Kafka, A/V real-time), platform-building initiative.
|
||||
|
||||
**Downplay / omit:** BDD, RPA, IBM ODM, Tibco Spotfire, BI/dashboard framing, semiconductor domain specifics, test automation as primary identity.
|
||||
|
||||
**User focus directives:** None given — using bundle Priority Matrix defaults.
|
||||
|
||||
---
|
||||
|
||||
## Critique Context (for /critique later)
|
||||
|
||||
**Reviewer persona:** Kraken Engineering hiring manager — likely a Rust-fluent senior infra engineer or EM. Will weight (a) production systems credibility, (b) Rust signal honesty (won't tolerate inflation), (c) MLOps maturity, (d) crypto enthusiasm in CL, (e) ability to operate 0→1 in fast-moving teams.
|
||||
|
||||
**Competitive landscape:** Pool likely includes Rust-native backend engineers from FAANG / crypto-native firms (Coinbase, Binance, Polygon) and ML infra engineers from AI labs. Dennis competes by leading with MLOps + production reliability + cloud-native depth — and being honest about Rust as building.
|
||||
|
||||
**Domain vocabulary:** model inference, orchestration, execution layer, agent systems, model serving, evaluation frameworks, guardrails, async, Tokio, MCP, observability, SLO, latency budget, throughput, p99.
|
||||
|
||||
---
|
||||
|
||||
## Cover Letter Plan
|
||||
|
||||
**Institution type:** Crypto-native, Rust-heavy, production-engineering-focused.
|
||||
|
||||
**Length:** 1 page, 250-300 words.
|
||||
|
||||
**Paragraph structure:**
|
||||
1. **Hook (2-3 sentences):** Open with Kraken-customer-since-2017 line + Solidity in free time — establishes genuine crypto-native identity from sentence one. Then pivot to professional fit: I've followed Oxidizing Kraken Parts 1 & 2 and the Kraken CLI launch — the AI Infrastructure team's mandate is what I'd want to work on regardless of company.
|
||||
2. **Production ML credibility:** Bosch BS-1 — designed and deployed ML inference into a 24/7 semiconductor fab; the operational constraint (no maintenance windows, hardware-in-the-loop) is what shapes how I think about model-serving infrastructure.
|
||||
3. **Cloud-native + observability + scale:** Swisscom (Switzerland's largest telco) — owning K8s-deployed Python data services on AWS, Kafka-based streaming, plus the observability stack at Bosch (ELK + Prometheus + Grafana). Tie to "high request throughput, observability, failure recovery."
|
||||
4. **Honest on Rust:** One short, candid sentence — systems-level background is C++ (Vizrt distributed transcoding); building Rust depth currently. No inflation.
|
||||
5. **Close:** Switzerland-based (location match); long-time Krakenite as a customer, would be excited to be one as an engineer.
|
||||
|
||||
**Hooks (specific to research):**
|
||||
- **Long-term Kraken customer since 2017** (BTC + ETH); active user of Kraken / Kraken Pro / Krak apps — primary CL opener
|
||||
- **Solidity smart-contract development in free time** — concrete proof of crypto-native engineering interest, not just trading
|
||||
- "Oxidizing Kraken Parts 1 & 2" — millions of lines of Rust across hundreds of services, async Tokio migration in 2020-21
|
||||
- Kraken CLI (Nov 2025) — first crypto CLI built for AI agents, MCP-native
|
||||
- Mission: financial freedom and inclusion via crypto
|
||||
|
||||
**Jargon level:** High — technical reader. Use Tokio, async, MCP, model inference, p99, observability comfortably.
|
||||
|
||||
**Avoid in CL:** SCEDAS / maritime / BDD / RPA / Tibco / semiconductor domain depth (mention Bosch, but lead with the ML deployment angle, not the wafer/fab specifics).
|
||||
|
||||
---
|
||||
|
||||
## Bundle Selection Rationale
|
||||
|
||||
- **Primary: ML/AI Engineer (`bundle_ml_ai_engineer.md`)** — JD title and team mission are AI Infrastructure / agent systems / model inference. Priority Matrix and Reframing Map align directly.
|
||||
- **Secondary: Data Platform/Infra (`bundle_data_platform.md`)** — for the distributed systems / observability / Kubernetes / cloud-native framing. Use to bridge 1-2 bullets toward the systems-engineering side of the JD (e.g., reframe SW-3 with platform-leaning language; pull BS-4 observability framing).
|
||||
|
||||
---
|
||||
|
||||
## Output Files (planned)
|
||||
|
||||
- `e2e_kraken_ai_infra_resume.tex` — 2-page resume
|
||||
- `e2e_kraken_ai_infra_cover_letter.tex` — 1-page cover letter
|
||||
- `critique_kraken_ai_infra.md` — critique output
|
||||
|
||||
---
|
||||
|
||||
## Bullet Plan (CONFIRMED 2026-05-01)
|
||||
|
||||
**Final: 18 variable bullets across 5 positions** (2 added during page-fill gate: SW-4 + GN-2).
|
||||
|
||||
| Position | Bullets | IDs | Notes |
|
||||
|---|---|---|---|
|
||||
| Swisscom | 6 | SW-2, SW-1, **SW-GenAI (corrected: LiteLLM/Kiro/custom GPTs — no LangChain)**, SW-3, SW-6, SW-4 | SW-4 added for page fill |
|
||||
| Bosch | 4 | BS-1, BS-4, BS-3, BS-2 | BS-1 leads (24/7 ML inference) |
|
||||
| Fraunhofer | 3 | FC-2, FC-1, FC-3 | |
|
||||
| Vizrt | 2 | **VZ-1 (Python-led, C++ legacy parenthetical)**, VZ-2 | C++ unbold per user feedback |
|
||||
| Generali | 3 | GN-1, **GN-2 (added)**, GN-3 | GN-2 added for page fill |
|
||||
|
||||
**Skills section:** 5 groups including a **Crypto / Web3** line (Solidity smart contracts, Ethereum, Kraken long-term user) — confirmed by user. C++ kept in languages but unbold.
|
||||
|
||||
**Forced exclusions:** SW-4 (B2B dashboards — weak for AI infra), SW-5 (Security Champion — only 2025/26 per memory, off-theme), BS-5 (Tibco — irrelevant), FC-4 (grant proposal — weak), GN-2 (UIPath RPA — irrelevant).
|
||||
|
||||
**Budget Gate:** Target 20-21 from `resume_reference.md`; user accepted 16 for quality > quantity. Skills section will absorb the slack (slightly fuller skills block compensates for fewer bullets). PASS.
|
||||
|
||||
---
|
||||
|
||||
## Status
|
||||
|
||||
- Phase 0: DONE
|
||||
- Phase 1: DONE (18 bullets final, after page-fill adjustment from 16)
|
||||
- Phase 2: **DONE** — compiled, 2 pages, 18 bullets, all char counts within budget
|
||||
- CL: **DONE** — compiled, 1 page, ~285 words, 2 em-dashes, all hooks verified
|
||||
- Critique: **CURRENT** (Pass 2 = 84.5/100; all Pass 1 Tier 1 fixes verified applied)
|
||||
|
||||
**Critique summary (Pass 2):**
|
||||
- Score trajectory: Pass 1 81.5 → Pass 2 84.5 (+3.0). Converged near theoretical max ~86; hard ceiling ~88 (Rust gap).
|
||||
- All three Pass 1 Tier 1 fixes verified in compiled PDF: summary crypto/Solidity hook lands at recruiter-glance speed; B3 carries "agent assistants" + "LLM API gateway, model routing"; B6 reframed to ML/analytics consumers (no B2B dashboards).
|
||||
- ATS match 76% → ~80%. Compile clean (2pp resume + 1pp CL). AI fingerprint clean (em-dashes 1+2, no banned words, no -ing endings).
|
||||
- No Tier 1 fixes remaining. Tier 2 polish optional: (a) add "agent orchestration / guardrails" to skills group #1, (b) CL active-bridge closer, (c) trim B4 -7 chars.
|
||||
- Verdict: **Submit-ready as-is.** Tier 2 only if a polish round desired.
|
||||
|
||||
**Output Files:**
|
||||
- `e2e_kraken_ai_infra_resume.tex` / `.pdf` — 2 pages, 176KB
|
||||
- `e2e_kraken_ai_infra_cover_letter.tex` / `.pdf` — 1 page, 143KB
|
||||
- `resume.cls` — copied locally for compilation
|
||||
|
||||
**Hook verification log (CL):**
|
||||
- "Oxidizing Kraken" — verified via blog.kraken.com (Feb 2021, Simon Chemouil)
|
||||
- "Kraken CLI MCP-native for Claude, Cursor, Codex" — verified via github.com/krakenfx/kraken-cli
|
||||
- Kraken customer since 2017 + Solidity — personal claim from user memory (user_crypto.md)
|
||||
|
||||
**Next:** `/clear` then `/critique output/Kraken_AI_Infrastructure/session_kraken_ai_infra.md`
|
||||
Reference in New Issue
Block a user