Vice President, AI Engineering Team Lead

About the role

MUFG is seeking a Vice President, AI Engineering Team Lead to join its Corporate Technology division in London. The role leads a team of AI Engineers in the Digital Delivery pillar, shaping and scaling an emerging AI Centre of Excellence that designs and delivers high-value AI solutions across EMEA, the US and Asia. It involves end-to-end accountability for solution design, delivery and support, with strong emphasis on compliance in a regulated environment.

Key responsibilities

• Guide a team of around five AI Engineers in designing, developing, deploying and supporting production-grade generative AI solutions, including RAG architectures and agentic systems.
• Work with Digital Transformation teams and business stakeholders to understand requirements, prioritise use cases, turn demand into roadmaps and plan capacity.
• Establish architectural principles, engineering standards, reusable patterns and quality controls for AI solutions in a regulated environment.
• Evaluate platforms such as Microsoft Azure or AWS against business value, cost, risk and long-term supportability.
• Manage the team's development lifecycle: backlog, estimates, milestones, risks, documentation, release readiness and reporting.
• Ensure compliance with policies, including change management and privileged production access, and set technical guardrails and controls.
• Provide high-quality support for team services, define success measures with business owners and track outcomes.
• Build a customer-centric team, promote responsible AI and champion new technologies and best practices across the wider business.

Required profile

• Proven experience leading engineering teams with line management responsibilities in complex, collaborative environments.
• Demonstrated ability to deliver production-grade generative AI solutions (RAG, vector databases, model evaluation).
• Deep expertise in Microsoft Azure or AWS, with Secure by Design, Architecture Review Board, DPIA and CI/CD knowledge.
• Ability to define technical standards, document architectural decisions and assess vendors and delivery approaches.
• Knowledge of security, privacy, responsible AI, data governance and model risk, suited to regulated industries.
• Excellent communication: presenting technical strategy and business cases to senior leadership and defining measurable outcomes.
• Degree in Computer Science or Data Science, or equivalent practical experience.
• Background in machine learning, data science, MLOps or LLMOps is highly desirable; experience in 1st/2nd/3rd line support across business areas is an advantage.

Required skills

• Microsoft Azure or AWS
• RAG architectures, vector databases, model evaluation frameworks
• CI/CD pipelines
• Orchestration and governance tools for AI engineering, including GitHub Copilot
• Jira and Confluence (advantageous)

What we offer

• Generous training opportunities and access to a global network of experts.
• Flexible working arrangements.
• A supportive, inclusive culture with knowledge sharing across EMEA, the US and Asia.