AI Embedment Lead

About the role

Shell is seeking an AI Embedment Lead to join its team in Houston, TX. The ideal candidate combines deep expertise in petrophysics and production engineering with the ability to bridge industry knowledge and practical AI development and implementation. This role sits within a highly skilled and dynamic team of domain, AI/ML, and analytics experts focused on identifying the most impactful opportunities and delivering measurable results at pace. The team operates across Shell’s global portfolio and is empowered to challenge conventional approaches, test new ideas, and rapidly configure, customize, and deploy solutions in live asset environments. Individuals in this team are expected to operate with a high degree of autonomy and creativity, embracing ambiguity and taking calculated risks to unlock value. Success is defined not only by technical excellence but also by the ability to push boundaries, move quickly from concept to execution, and drive tangible business outcomes. The AI Embedment Lead is responsible for identifying, adapting, implementing, and embedding AI-driven solutions within Shell’s operated upstream oil and gas assets to deliver measurable operational and financial value. This role bridges domain expertise and advanced AI capabilities, ensuring solutions are not only technically sound but also aligned with real business challenges and successfully adopted in the field. The position focuses on developing bespoke, high-impact solutions, including generative AI tools, agentic AI workflows, and advanced analytics. The successful candidate will work closely with asset teams to configure, customize, deploy, and scale solutions while identifying opportunities for repeatability and value creation across multiple assets.

Responsibilities

• Partner with asset leadership, subsurface, wells, and operations teams to identify business problems where AI can create meaningful value
• Apply strong subsurface & wells domain knowledge to prioritize use cases with clear operational or economic impact
• Translate ambiguous business challenges into actionable digital/AI solution opportunities
• Act as a translator between innovation & products teams and business stakeholders
• Ensure AI solutions reflect the realities of subsurface & wells workflows (e.g., uncertainty, data quality, decision cycles)
• Provide domain-informed guidance on model design, outputs, and usability
• Lead the implementation of generative AI solutions, including custom code and workflows
• Lead the implementation of agentic AI systems, ranging from task automation to semi-autonomous decision support
• Lead the implementation of advanced analytics solutions, including data ingestion, pattern recognition, and predictive insights
• Collaborate with data scientists, engineers, and platform teams to build robust solutions
• Work directly within assets to ensure solutions are embedded into day-to-day workflows
• Drive adoption through training, change management, and iterative refinement
• Ensure solutions are operationally reliable, scalable, and user-centric
• Identify components of bespoke solutions that can be reused or scaled across other assets
• Contribute to building reusable frameworks, libraries, and best practices
• Share learnings across the organization to accelerate adoption and value capture
• Ensure solutions adhere to company standards for data governance, security, and responsible AI
• Validate outputs and ensure appropriate human oversight in decision-making contexts

Requirements

• Must have legal authorization to work in the US on a full-time basis for anyone other than current employer
• Bachelor's degree required; MS/PhD in Geoscience or Petroleum Engineering, Reservoir Engineering, or related discipline is preferred
• At least 10 years of relevant experience in the Upstream Oil & Gas industry is preferred
• Deep expertise in petrophysics / formation evaluation or production engineering / production technology; other subsurface and wells disciplines considered
• Proven experience developing, implementing, or building and deploying Agentic AI and Generative AI (GenAI) solutions