1200 Equinor ASA
Important Notice Please submit your application before the end of the day on 05.06.2026. We encourage candidates to apply as soon as possible. What does the job involve? Shape how Equinor designs and scales agentic AI solutions-working at the intersection of architecture, engineering, and responsible AI. Equinor is building the foundation for enterprise scale agentic AI, and this role offers a rare opportunity to play a key role in that journey. You will architect solutions, make key design decisions, guide delivery with AI and ML engineering teams, and ship production systems that automate workflows and sharpen decisions across multiple business areas. What will my tasks be? Shape and apply reference architecture for agentic systems, translating standards, design principles, and guardrails into practical solution patterns across Equinor. Architect and deliver production agents: orchestration, grounded retrieval, structured LLM integrations with enterprise APIs, MCP based tool/data access, and multimodal document understanding. Drive solution strategy, technology choices, and architectural blueprints for key use cases; align stakeholders, engineering teams, and partners; and provide technical leadership from 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 the bar on engineering quality and long term capability by improving performance, reliability, and cost efficiency, while mentoring peers and building reusable foundations for future AI solutions. What do we expect from you? Overall qualities: open, collaborative, courageous and caring. We want you to identify with the values that guide our decisions and help us succeed and grow, and to make safety your priority and contribute to our zero harm culture. Required qualifications Master's or PhD in Computer Science, Data Science, Machine Learning, Linguistics, or a related field. Strong architectural experience across data, model, and application layers, with sound judgment on trade offs, scalability, risk, and compliance in enterprise AI systems. Proven ability to navigate ambiguity, set technical direction within a broader architecture, align stakeholders, and convert strategy into delivery. Hands on 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). Track record of delivering AI solutions from concept to production, with measurable business impact. Preferred qualifications Technical leadership: mentoring, communities, publishing, speaking, or open source contributions. Multimodal AI: document and image understanding, diagram Q&A, speech to text. 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). Integration: APIs, events/messaging, standardised data and tool access via MCP. Why join us? Motivated by architecting practical AI solutions, working on technically demanding challenges, and helping scale responsible agentic AI in a major energy company? We would love to hear from you. What can we offer you? Not just a job - a career: In Equinor, your development begins on day one. You will build competence through a wide range of learning activities while being empowered to build your career across multiple disciplines and geographies. Attractive rewards: 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 employees' well being. Flexible work arrangements are encouraged wherever possible. An inclusive culture: Embracing differences makes us stronger. Everyone can bring their whole self to work and feel accepted and valued. Equal opportunities for everyone 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 are made during the recruitment process for candidates with disabilities or long term health conditions. If you have any specific requirements, please clarify in your application and our team will be in contact to see how we can support your needs. All positions are evaluated using our global job architecture based on responsibilities, complexity, and impact, and reviewed regularly to ensure fairness and consistency.
Important Notice Please submit your application before the end of the day on 05.06.2026. We encourage candidates to apply as soon as possible. What does the job involve? Shape how Equinor designs and scales agentic AI solutions-working at the intersection of architecture, engineering, and responsible AI. Equinor is building the foundation for enterprise scale agentic AI, and this role offers a rare opportunity to play a key role in that journey. You will architect solutions, make key design decisions, guide delivery with AI and ML engineering teams, and ship production systems that automate workflows and sharpen decisions across multiple business areas. What will my tasks be? Shape and apply reference architecture for agentic systems, translating standards, design principles, and guardrails into practical solution patterns across Equinor. Architect and deliver production agents: orchestration, grounded retrieval, structured LLM integrations with enterprise APIs, MCP based tool/data access, and multimodal document understanding. Drive solution strategy, technology choices, and architectural blueprints for key use cases; align stakeholders, engineering teams, and partners; and provide technical leadership from 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 the bar on engineering quality and long term capability by improving performance, reliability, and cost efficiency, while mentoring peers and building reusable foundations for future AI solutions. What do we expect from you? Overall qualities: open, collaborative, courageous and caring. We want you to identify with the values that guide our decisions and help us succeed and grow, and to make safety your priority and contribute to our zero harm culture. Required qualifications Master's or PhD in Computer Science, Data Science, Machine Learning, Linguistics, or a related field. Strong architectural experience across data, model, and application layers, with sound judgment on trade offs, scalability, risk, and compliance in enterprise AI systems. Proven ability to navigate ambiguity, set technical direction within a broader architecture, align stakeholders, and convert strategy into delivery. Hands on 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). Track record of delivering AI solutions from concept to production, with measurable business impact. Preferred qualifications Technical leadership: mentoring, communities, publishing, speaking, or open source contributions. Multimodal AI: document and image understanding, diagram Q&A, speech to text. 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). Integration: APIs, events/messaging, standardised data and tool access via MCP. Why join us? Motivated by architecting practical AI solutions, working on technically demanding challenges, and helping scale responsible agentic AI in a major energy company? We would love to hear from you. What can we offer you? Not just a job - a career: In Equinor, your development begins on day one. You will build competence through a wide range of learning activities while being empowered to build your career across multiple disciplines and geographies. Attractive rewards: 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 employees' well being. Flexible work arrangements are encouraged wherever possible. An inclusive culture: Embracing differences makes us stronger. Everyone can bring their whole self to work and feel accepted and valued. Equal opportunities for everyone 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 are made during the recruitment process for candidates with disabilities or long term health conditions. If you have any specific requirements, please clarify in your application and our team will be in contact to see how we can support your needs. All positions are evaluated using our global job architecture based on responsibilities, complexity, and impact, and reviewed regularly to ensure fairness and consistency.
1200 Equinor ASA
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.
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.