AI Embedment Lead

, United Kingdom

Job Family Group:

Geosciences

Worker Type:

Regular

Posting Start Date:

October 19, 2026

Business Unit:

Experience Level:

Experienced Professionals

Job Description:

What’s the role

Shell is seeking an AI Embedment Lead to join its team in London. 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.

What you’ll be doing

Together with together with our customer success team, 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

Agentic AI systems, ranging from task automation to semi-autonomous decision support

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

What you bring

Bachelor's degree required; MS/PhD in Geoscience or Petroleum Engineering, Reservoir Engineering, or related discipline is preferred

Extensive years of 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

Strong proficiency in Python and AI/ML solution development

Hands-on building of LLM applications and agentic systems, including evaluation of their outputs

Regular use of AI coding assistants and agents

Experience with data analytics, statistics, and/or visualization tools such as Databricks, Plotly Dash, and Spotfire

Knowledge of data pipelines, solution architecture, and deployment best practices

Proven experience delivering production-ready solutions

Design-thinking mindset with a track record of driving technology innovation and adoption

Excellent communication, collaboration, and relationship-building skills across cross-functional teams and stakeholders