# Session: Equinor — Manager / AI Architect, Agentic Systems

## JD Info
- **File:** JDs/equinor_ai_manager.txt.txt
- **Role:** Manager / AI Architect – Agentic systems
- **Company:** Equinor (international energy company, HQ Norway; net-zero-by-2050 ambition; ~25k staff)
- **Bundle:** ML / AI Engineer (Tier 2)
- **Format:** Resume (2-page, resume.cls) + 1-page cover letter
- **Salary/Details:** Norway-based (Stavanger / Oslo / Rotvoll / Sandsli). Apply by 05.06.2026. Comp scheme: competitive salary + bonus + pension. Relocation in scope (user worked 1 yr in Norway, Norwegian basic).
- **Strategic note:** HONEST STRETCH. Required agent-framework + GenAI-architecture + management evidence is thin. Position as senior production-AI/MLOps engineer with architecture breadth reaching toward agentic-AI architect — NOT a checkbox match. Accept reach-application odds.

## JD Analysis
### Requirements
| # | Requirement | Match | Evidence |
|---|-------------|-------|----------|
| 1 | Master's/PhD in CS/DS/ML/Linguistics | Direct | M.Eng. Computer Aided Engineering (Software Design & Engineering) |
| 2 | Strong architectural experience across data/model/app layers; trade-offs, scalability, risk, compliance | Bridge HIGH | **Data Mesh / data products / metadata mgmt on AWS (SW-7) = data layer for agents**; cloud-native, K8s, ETL ownership; security/compliance ownership; model-layer still weaker |
| 3 | Navigate ambiguity, set technical direction, align stakeholders, strategy→delivery | Bridge MED | Staff/Component Owner, B2B stakeholder data products, elected Leadership Cohort, mentoring |
| 4 | Hands-on modern LLMs + agent frameworks (LangGraph, AutoGen, LangChain/LlamaIndex, Semantic Kernel) | GAP | LiteLLM gateway + custom GPTs + Kiro (agentic IDE) only. **Never used named frameworks — do NOT list.** |
| 5 | NLP + GenAI: prompt design, RAG, model eval, LLM providers + OSS | Bridge LOW | Custom GPTs (prompt/grounding), LiteLLM multi-provider gateway; Fraunhofer NLP/ASR (dated, contributing dev) |
| 6 | Solid Python + SWE fundamentals (testing, CI/CD, version control) | Direct | Python primary, GitLab CI/CD, K8s, DevOps lifecycle ownership |
| 7 | Track record delivering AI concept→production, measurable business impact | Bridge HIGH | Bosch: ML inference deployed into 24/7 semiconductor fab (zero-downtime production ML) |
| P1 | Technical leadership: mentoring, communities, publishing, speaking, OSS | Bridge LOW | Mentoring + Leadership Cohort; no publishing/OSS/speaking |
| P2 | Multimodal AI: doc/image understanding, diagram Q&A, speech-to-text | Bridge MED | Bosch image defect classification (image) + Fraunhofer ARTUS speech recognition (speech-to-text) |
| P3 | Responsible AI: PII, red-teaming, content moderation, risk, regulatory compliance | Bridge MED | Security Champion (DevSecOps, risk monitoring), data governance, GDPR/privacy-by-design; no red-teaming |
| P4 | Containers + cloud: Docker, K8s; Azure (Azure ML, AKS, Azure OpenAI) | Partial | Docker/K8s Direct; **Azure GAP — AWS-heavy, do NOT claim Azure** |
| P5 | Integration: APIs, events/messaging, MCP | Bridge MED | Kafka events/messaging (Direct), LiteLLM API gateway; MCP conceptual via Kiro/Claude tooling |

### ATS Keywords
- **AI/ML:** agentic AI, LLM, RAG, prompt design, model evaluation, production ML, ML inference, MLOps, NLP, speech recognition, image classification, multimodal
- **Architecture:** reference architecture, solution patterns, orchestration, guardrails, scalability, reliability, cost efficiency
- **Responsible AI:** responsible AI, GDPR, EU AI Act, privacy by design, policy as code, risk assessment, PII, human oversight, fallback/rollback
- **Tools:** Python, Docker, Kubernetes, CI/CD, GitLab, Kafka, MCP, LLM API gateway (LiteLLM), enterprise APIs
- **Soft:** technical leadership, stakeholder alignment, mentoring, concept-to-production, ambiguity, cross-functional

### Gap Assessment
- **Direct:** Master's, Python + SWE/CI-CD, Docker/Kubernetes, events/messaging (Kafka), concept-to-production delivery (industrial ML)
- **Bridge:** architecture breadth (data/app/infra), stakeholder/technical leadership, multimodal (image + speech), responsible AI (security/governance/GDPR), MCP/API integration, LLM integration (LiteLLM/custom GPTs)
- **Gap (do NOT claim):** named agent frameworks (LangGraph/AutoGen/LangChain/LlamaIndex/Semantic Kernel); Azure (Azure ML/AKS/Azure OpenAI); production RAG + formal model evaluation; formal people-management; red-teaming/content moderation

## Company Context
- **Mission:** International energy company, HQ Norway; leading the energy transition, net zero by 2050; energising ~170M people.
- **This role:** Build the foundation for enterprise-scale agentic AI — shape reference architecture, architect/deliver production agents (orchestration, grounded retrieval, MCP tool access, multimodal doc understanding), embed responsible AI (GDPR/EU AI Act, policy-as-code, bounded autonomy, human oversight, safe fallback/rollback), raise engineering quality/reliability/cost, mentor peers.
- **Culture:** Values — open, collaborative, courageous, caring; safety-first / zero-harm culture. Norway-based, strong work-life balance, internal mobility.
- **Tech reality (web research):** Omnia data platform on **Azure**; EurekaML (Azure ML/Synapse/Cosmos) cut model deploy 1–2 wks → 1 day; **Prevent: 520+ ML models in production**, millions of predictions/day, detecting rotating-equipment failures; Knowledge AI team doing NLU.
- **"Why them" angle:** Equinor already runs industrial ML at scale on safety-critical physical assets — exactly the regime Dennis shipped at Bosch (ML inference in a 24/7 wafer fab, zero downtime tolerance). The agentic layer is new for everyone (only ~11% of enterprises in production per 2026 data); Dennis brings the production-reliability + responsible-AI discipline that separates demos from deployed systems. Norwegian + prior year working in Norway = genuine relocation/culture fit.

## Framing Strategy
- **Lead narrative:** "Production-AI/MLOps engineer who ships and operates ML/AI in zero-downtime industrial environments — pairing deployment reliability, security/responsible-AI ownership, and hands-on LLM-integration + agentic-tooling experience." Reach toward architect/manager via Staff-level ownership + stakeholder leadership, WITHOUT claiming agent-framework or Azure depth.
- **Reframing map:**
  - Data Mesh / data products / metadata mgmt on AWS (SW-7) → "reference architecture for agentic systems; governed, discoverable data layer that agents query (grounded retrieval / MCP tool-data access / speak-to-data)" — LEAD agentic bridge, mirrors Equinor Omnia
  - Bosch ML inference in 24/7 fab → "production AI from concept to deployment; reliability, safe rollout in safety-critical ops" (mirrors Equinor Prevent)
  - Bosch image defect classification → "multimodal: image understanding in production"
  - Fraunhofer ARTUS → "applied NLP / speech-to-text (contributing dev)" — multimodal preferred qual
  - LiteLLM → "structured LLM integration with enterprise APIs via self-built LLM gateway (multi-provider)"
  - Custom GPTs + grounded domain knowledge → "prompt design + grounded retrieval over enterprise knowledge" (hedge — internal GPTs, not production RAG)
  - Kiro (agentic AI IDE) → "hands-on with agentic dev tooling / MCP-based tool access" (conceptual MCP)
  - Security Champion + data governance → "responsible AI: privacy-by-design, GDPR, risk monitoring, policy-as-code mindset, safe execution"
  - K8s + GitLab CI/CD + on-call SLA → "engineering quality, reliability, cost efficiency; safe fallback/rollback"
  - Kafka → "events/messaging integration"
  - Component Owner + B2B data products + Leadership Cohort + mentoring → "technical direction, stakeholder alignment, mentoring"
- **Emphasize:** Bosch production ML (LEAD), LiteLLM LLM gateway, Security/responsible-AI, K8s/Docker/CI-CD, multimodal (image+speech), stakeholder/technical leadership.
- **Downplay:** Pure ETL/BI framing, Teradata legacy detail, test automation, anything implying Azure or named agent frameworks.
- **CL hooks:** Equinor Prevent (520+ production models, rotating equipment) ↔ Bosch 24/7 fab ML; agentic layer is early everywhere → bring production-reliability + responsible-AI discipline; Norway relocation + Norwegian basic + prior year in Norway; safety/zero-harm culture ↔ Security Champion + zero-downtime ops.
- **User directives:** NEVER list LangChain/LangGraph/LlamaIndex/Semantic Kernel/AutoGen. Security Champion = 2025/26 ONLY (not 3 yrs). No Azure claims. No fabricated tools. Don't oversell C++.

## Critique Context
- **Reviewer persona:** Equinor AI/architecture hiring lead or principal engineer — runs production ML at scale (Prevent/EurekaML), Azure-native, pragmatic about the demo-to-production gap. Impressed by: real production ML under operational constraints, reliability/safety rigor, honest responsible-AI substance. Bored/alarmed by: buzzword agent-framework name-dropping with no depth, inflated GenAI claims.
- **Competitive landscape:** "Obvious fit" = someone who has shipped LLM agents with LangGraph/Semantic Kernel on Azure OpenAI + RAG in production. We do NOT have that. Our edge: rarer industrial zero-downtime production-ML credibility + security/governance discipline + Norway fit. We win on reliability/responsibility, not on agentic-framework hours.
- **Domain vocabulary:** orchestration, grounded retrieval, MCP, guardrails, bounded autonomy, human-in-the-loop, policy-as-code, EU AI Act, reference architecture, Omnia/Prevent (Equinor-specific — use sparingly, only in CL).

## Cover Letter Plan
- **Institution type:** Industry — major energy company, applied AI/architecture org
- **Paragraph count:** 4 paragraphs, ~280 words (1 page)
- **P1 hook:** Equinor Prevent / industrial ML at scale ↔ my Bosch 24/7 fab production-ML deployment (zero downtime). Establish production credibility + honest pivot framing.
- **P2-P3 evidence:** LiteLLM LLM gateway + custom grounded GPTs + agentic dev tooling (LLM integration); Security Champion + data governance (responsible AI, GDPR, safe execution); K8s/CI-CD reliability. Honest about reaching toward agentic architecture, leading with production discipline.
- **Domain pivot:** "The agentic layer is new for nearly everyone — what I bring is the production-reliability and responsible-AI discipline that turns prototypes into systems you can trust on safety-critical assets."
- **Jargon level:** Technical but HR-safe
- **Hook verification (DONE):** Prevent = Omnia.Prevent, 520+ ML models in production on rotating equipment; AI saved Equinor ~$130M in 2025 via predictive maintenance. Source: equinor.com/energy/machine-learning + rigzone.com (07 Jan 2026). Net-zero-2050 / 170M people / Norway locations / zero-harm culture direct from JD.
- **"Why them" hook:** Energy transition + Norway relocation (prior year working in Norway, Norwegian basic) + safety/zero-harm culture ↔ my security-first, zero-downtime track record.

## Bullet Plan (CONFIRMED — 15 bullets)

### Position 1 — Swisscom (6 bullets) — theme: Agentic-AI Data Foundations, LLM Integration & Responsible-AI Engineering
| # | ID | Achievement | Variant |
|---|-----|------------|---------|
| 1 | SW-7 | Data Mesh + data products + metadata mgmt on AWS = agentic data foundation (LEAD) | 2L |
| 2 | SW-GenAI | Custom GPTs + LiteLLM gateway + Kiro agentic assistants (LLM-to-enterprise-API) | 2L |
| 3 | SW-5 | Security Champion 2025/26 + data governance → responsible AI, GDPR, policy-as-code | 2L |
| 4 | SW-1 | AWS cloud-native migration (S3, Glue, Athena/Iceberg, Redshift, Airflow, CloudFormation) | 2L |
| 5 | SW-3 | Python services on Kubernetes + GitLab CI/CD (reliable rollout/rollback) | 2L |
| 6 | SW-2 | Component Owner Fulfillment ETL (Oracle/Kafka→Teradata), SLA + governance | 2L |

### Position 2 — Bosch (4 bullets) — theme: Production ML from Concept to Deployment in 24/7 Manufacturing
| # | ID | Achievement | Variant |
|---|-----|------------|---------|
| 1 | BS-1 | ML inference into 24/7 fab (Docker/K8s/Ansible), automated image defect classification (LEAD) | 2L |
| 2 | BS-4 | ELK + Kafka anomaly detection; Grafana/Prometheus/Loki observability | 2L |
| 3 | BS-3 | Application Owner — SLOs, vendor mgmt, training, docs | 2L |
| 4 | BS-2 | Python/Java/C# data services over Oracle/Hadoop | 2L |

### Position 3 — Fraunhofer (2 bullets) — theme: Applied NLP, Speech Recognition & Microservice Engineering
| # | ID | Achievement | Variant |
|---|-----|------------|---------|
| 1 | FC-2 | ARTUS — ML/NLP speech-to-text for sea-rescue transcription (hedged: contributed) | 2L |
| 2 | FC-3 | MISSION microservices (Express.js, Docker) — data-exchange APIs | 2L |

### Position 4 — Vizrt (2 bullets, Bergen NORWAY) — theme: Distributed Real-Time Backend Engineering
| # | ID | Achievement | Variant |
|---|-----|------------|---------|
| 1 | VZ-1 | Distributed real-time transcoding backend (Python, legacy C++) — CNN/BBC/Al Jazeera | 2L |
| 2 | VZ-2 | A/V test suite + CI/CD quality gates | 2L |

### Position 5 — Generali (1 bullet) — theme: Technical Leadership, Test Automation & Java Backend
| # | ID | Achievement | Variant |
|---|-----|------------|---------|
| 1 | GN-1 | Introduced BDD + trained teams / Java Community (technical leadership) | 2L |

**Budget:** 15 variable bullets (30 rendered lines) + ~17-18 skill lines. Reserves if page under-fills: FC-1 (Jenkins CI/CD), GN-3 (Java/J2EE).
**Forced exclusions:** LangChain/LangGraph/LlamaIndex/Semantic Kernel/AutoGen; Azure; Solidity/Kraken crypto. Security Champion 2025/26 only.

## Output Files
- Resume: `output/Equinor_AI_Manager/e2e_equinor_ai_manager_resume.tex`
- Cover Letter: `output/Equinor_AI_Manager/e2e_equinor_ai_manager_cover_letter.tex`
- Critique: `output/Equinor_AI_Manager/critique_equinor_ai_manager.md`

## Status
- Phase 0: DONE
- Phase 1: DONE (17 bullets confirmed — added reserves FC-1, GN-3 for page fill)
- Phase 2 Resume: DONE — compiled 2 pages, all bullets within char limits, 0 em-dashes, no -ing endings
- Cover Letter: DONE — compiled 1 page, 313 words, 0 em-dashes, no banned words, hooks verified (Prevent 520+ models / $130M). 4 paragraphs.
- Critique: CURRENT — **Pass 2 = 80.0/100** (Pass 1 77.5 → +2.5). Converged near achievable ceiling (~81, metrics excluded per user; hard ceiling ~83). No remaining Tier 1 fixes.
- **Next:** Submit — run finalization (rename PDFs for submission).

## Critique Summary (Pass 1, 2026-05-27)
- **Score:** 77.5/100 → +Tier1 ≈80.5 → max ≈82 → hard ceiling ≈83.
- **Compile:** Resume 2pp clean; CL 1pp clean. 0 em-dashes in output (17 `---` hits are LaTeX comment dividers). All bullets within char limits. Page 2 ~30% underfill (cosmetic, skip).
- **Interview likelihood:** ATS ~80%, Recruiter ~55%, HR ~50%, HM ~30-40%, Tech panel CONCERNS-but-credible.
- **Tier 1 fixes (do these, ~+2 pts) — REVISED per user 2026-05-27:**
  1. **SW-7 verb fix + data-layer architecture** (accuracy + on-title win). Data Mesh/ODP is a company-wide migration Dennis contributed to, NOT solo-built. Approved rewrite (216 chars): "Helped move Swisscom from legacy Teradata to a cloud **Data Mesh** on **AWS**; modelled and built governed **data products** and onboarded source systems onto the foundation AI and **agentic workflows** query for grounded retrieval." + summary hedge ("design and build governed data products on the company's AWS Data Mesh"; opener "Software and AI engineer"). **Do NOT self-title "Solution Architect"** (certs = basic architecture grounding only). ~+1.2
  2. Add "orchestration" + "reference architecture / solution patterns" where truthful (skills + ODP framing). ~+0.8
  3. ~~Quantified metric~~ — **REMOVED: user does not want quantifications.** "Measurable business impact" gap accepted; ceiling ~81 with Tier 1.
- **Tier 2 (optional):** fix 3 participial bullet endings (B4 enabling / B8 providing / B10 supplying — AI fingerprint); trim CL ≤300 words.
- **Findings:** One overclaim caught (SW-7 sole-build → hedged, above). Otherwise clean: Security Champion 2025/26 ✓, no LangChain/agent frameworks ✓, no Azure ✓, languages correct ✓, Generali=Hamburg ✓. CL cohesion strong, all claims traceable.
- Critique file: `output/Equinor_AI_Manager/critique_equinor_ai_manager.md`

## Edit 1 Baseline (2026-05-27)
- Pages: 2
- Char violations: none (B6 208 / B12 211 / B16 209 = NEAR MAX but within 218)
- Orphan violations: none
- White space last page: ~30% of page 2 (acceptable for honest 2pp)
- Variable bullets: 17 (34 rendered lines)

### Edit 1 (2026-05-27): Tier 1 + Tier 2 fixes
- **Changes (resume):**
  1. SW-7 verb fix (accuracy): "Built a Data Mesh…" → "Helped move Swisscom off legacy Teradata to a cloud **Data Mesh** on **AWS**; modelled and built governed data products and onboarded source systems that **agentic workflows** query for grounded retrieval." (193 chars, 2L; bold reduced to 29 so it fits 2 lines — first compile rendered 3L due to bold penalty, corrected).
  2. Summary hedge: "Software engineer" → "Software and AI engineer"; "I build the AWS Data Mesh and governed data products" → "I design and build governed data products on the company's AWS Data Mesh" (555 chars).
  3. Skills: added `orchestration` (Agentic/MLOps line, dropped redundant "model deployment") + `reference architecture` (Data Foundations line, dropped "data catalog").
  4. Tier 2 — fixed 3 participial `-ing` endings: B4 (enabling→for), B8 (providing→for; re-added "log aggregation"), B10 (supplying→that gave; +"reliable,").
- **Changes (cover letter):** trimmed 313→300 words; **fixed same sole-ownership phrasing for cohesion** ("I build the layer… an AWS Data Mesh… I own it" → "I build the data layer… governed data products on the company's AWS Data Mesh… I own them").
- **Source:** critique Tier 1.1/1.2 + Tier 2; user accuracy correction (Data Mesh ownership) + user "do Tier 2 now / keep onboarded".
- **Verification:** resume 2pp clean, CL 1pp clean; 0 char OVER, 0 orphans, 0 em-dashes in prose; CL 300 words; all `-ing` trailers gone; package cohesion intact.

| Metric | Before | After | Delta |
|--------|--------|-------|-------|
| Page count | 2 | 2 | 0 |
| Char violations | 0 | 0 | 0 |
| Orphans | 0 | 0 | 0 |
| White space pg2 | ~30% | ~30% | 0 |
| CL words | 313 | 300 | -13 |
