Sr. Manager / Lead Architect Agentic and Generative AI

Equinor ASA•Houston, TX

About The Position

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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.