Responsibilities
- Define and evolve the enterprise reference architecture for agentic and generative AI, establishing standards, decision principles, and guardrails that will shape how these capabilities scale across Equinor.
- Partner with AI and ML engineering teams to deliver production‑grade agentic systems: orchestration, grounded retrieval, structured LLM integrations with enterprise APIs, MCP‑based tool/data access, and multimodal document understanding.
- Lead solution strategy, technology choices, and architectural blueprints; align senior stakeholders, engineering teams, and strategic partners; and provide technical leadership from early concept through scaled deployment.
- Embed safety, compliance, and privacy by design; align with GDPR and the EU AI Act; enforce policy as code and safe tool execution; manage uncertainty in non‑deterministic systems by surfacing confidence, bounding autonomy, routing to human oversight, and providing safe fallback and rollback paths.
- Raise engineering quality and long‑term capability by driving performance, reliability, and cost efficiency while mentoring others and building reusable foundations for future AI solutions.
Required Qualifications
- Master’s or PhD in Computer Science, Data Science, Machine Learning, Linguistics, or related field.
- Deep architectural experience across data, model, and application layers, with strong judgment on trade‑offs, scalability, risk, and compliance in enterprise AI systems.
- Proven leadership in navigating ambiguity, shaping technical direction, aligning senior stakeholders, and translating AI strategy into business‑aligned execution.
- Hands‑on experience with modern LLMs and agent frameworks (e.g., LangGraph, AutoGen, LangChain/LlamaIndex, Semantic Kernel).
- NLP and generative AI expertise, including prompt design, RAG architectures, model evaluation, and practical experience with major LLM providers and open‑source models.
- Solid Python and software engineering fundamentals (testing, CI/CD, version control).
- A strong track record of delivering end‑to‑end AI solutions from concept to measurable business value.
Preferred Qualifications
- Recognized thought leadership through mentoring, publishing, speaking, patents, or stewardship of influential open‑source initiatives.
- Multimodal AI: document and image understanding, diagram Q&A, speech‑to‑text (e.g., Whisper).
- Responsible AI: PII handling, red‑teaming, content moderation, risk assessment, regulatory compliance.
- Containers and cloud: Docker, Kubernetes; Azure (Azure ML, AKS, Azure OpenAI, storage, networking).
- Enterprise integration: APIs, events/messaging, standardised data and tool access via MCP.
Benefits
- Comprehensive benefits package with competitive salary, global parental leave, bonus scheme and pension plan.
- Development opportunities: learning activities, internal job market for career growth across disciplines and geographies.
- Flexible work arrangements and work‑life balance support.
- Inclusive culture encouraging diversity, belonging, and respect.
Equal Opportunities
Equinor is an equal‑opportunity employer. We make all employment decisions without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, parental status, veteran status, or any other protected status. Reasonable adjustments will be made during the recruitment process for candidates with disabilities or long‑term health conditions.
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