Sr. Manager / Lead Architect Agentic and Generative AI

Requirements

• 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.
• Why join us?
• If you are motivated by the chance to shape enterprise AI at scale, solve technically demanding problems with real-world consequence, and leave a lasting mark on how a major energy company applies AI responsibly, we would love to hear from you.
• What can we offer you?
• We want you to have a rewarding and fulfilling work life. That's why we offer:
• Not just a job - a career
• Attractive rewards
• We give you a comprehensive benefits package with a competitive salary, global parental leave, bonus scheme and pension plan.
• Wellness and work-life balance
• We care about and prioritise our employees' well-being. We know that for you to be the best version of yourself in the workplace, being able to collect you

Benefits

Additional Information

Important! To make sure your application is considered, please submit it before the end of the day on (dd.mm.yyyy): 05.06.2026 We encourage candidates to apply as soon as possible. What does the job involve? Shape the AI foundation of one of Europe's leading energy companies-defining how agentic systems are designed, governed, and scaled for mission-critical impact. Equinor is building the foundation for enterprise-scale agentic AI, and this role offers a unique opportunity to set direction at company scale. You will define architecture and guide the delivery of agent and LLM-powered products that reshape how insight is generated, workflows are automated, and decisions are supported across engineering, operations, HSE, supply chain, and corporate functions. What will my tasks be? Within this position, your key tasks will be to: Define and evolve the enterprise reference architecture for agentic and generative AI, establishing the 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; and 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. Here's what we expect from you: At Equinor, there are some overall qualities we regard as essential. We want you to identify with the values that guide our decisions and help us succeed and grow: open, collaborative, courageous and caring. We expect you to make safety your priority and to contribute to our zero-harm culture. And for this specific position, we are also looking for: 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.

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