Equinor
Energy
Sr.Manager/LeadArchitectAgenticandGenerativeAI
“Sr. Manager / Lead Architect Agentic and Generative AI at Equinor. Skills: Agentic AI architecture, Generative AI, LLM integration, Enterprise AI systems. Define enterprise reference architecture for AI. Establish standards and guardrails for AI”
What You'll Achieve.
Mission-critical impact; Measurable business value; Lasting mark on AI application
Industry & Context.
Navigate ambiguity; Solve technically demanding problems
Background checks, Screening using sanctions lists, Affiliation to high-risk countries for security assessment
What They're Looking For.
Must Have
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 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 track record of delivering end-to-end AI solutions from concept to measurable business value
Nice to Have
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, Azure (Azure ML, AKS, Azure OpenAI, storage, networking), Enterprise integration: APIs, events/messaging, standardised data and tool access via MCP
What You'll Do.
Define enterprise reference architecture for AI
Establish standards and guardrails for AI
Partner with AI/ML teams
Deliver production-grade agentic systems
Lead solution strategy and technology choices
Align senior stakeholders and engineering teams
Provide technical leadership
Enforce policy as code
Manage uncertainty in non-deterministic systems
Raise engineering quality and capability
Build reusable foundations
How You'll Work.
Team & Collaboration
Partner with AI and ML engineering teams; Align senior stakeholders; Align engineering teams
Process & Methodology
Translate AI strategy into execution, Deliver end-to-end AI solutions
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