Vice President, AI Engineering Team Lead

Overview

As Vice President AI Engineering Team Lead in MUFG’s London corporate technology group, you will shape and scale an AI engineering team to deliver cutting-edge generative AI and ML solutions for diverse business domains. You’ll partner with Digital Transformation and stakeholders to translate requirements into practical roadmaps, establish engineering standards, and ensure regulatory alignment. You drive technical strategy, vendor evaluation, and cross-team collaboration to maximise business impact in a regulated financial services context. This role offers leadership, technical influence, and a meaningful chance to advance AI capabilities at a global bank.

Pay / Benefits

• flexible working opportunities
• generous pension contributions
• ongoing training opportunities
• inclusive and collaborative environment

Responsibilities

• Build and lead a team of ~5 AI engineers, setting objectives and priorities
• Oversee design, delivery and ongoing support of generative AI models and ML applications
• Collaborate with Digital Transformation and business stakeholders to shape opportunities and roadmaps
• Establish architectural principles, standards, reusable patterns, and quality controls
• Evaluate technologies, platforms, vendors, and delivery approaches for value and risk
• Manage end-to-end project lifecycles, backlogs, estimates, milestones, and risk
• Provide L1–L3 support across team services to ensure reliable operations
• Define success metrics and track outcomes using evidence-based methods
• Share knowledge and champion best practices across MUFG’s tech landscape
• Stay abreast of AI trends and guide cross-team initiatives across AMD
• Work with Digital Delivery to extend expertise and improve ways of working

Key requirements

• Extensive experience leading engineering teams in large organizations
• Strong background in building production-grade generative AI systems (RAG, vector databases, LLMOps)
• Deep experience with Azure or AWS AI platforms/services
• Solid understanding of ML lifecycles and enterprise application development
• Ability to set technical standards, make architectural decisions, and evaluate technologies/vendors
• Proven multi-tiered support across multiple business areas
• Knowledge of security/privacy/governance/model risk in regulated sectors
• Excellent communication with senior leadership and ability to influence decisions
• Proven track record of defining measurable outcomes and ROI using evidence-based methods
• Degree-educated in Computer Science or Data Science or related discipline or equivalent experience

• Strong leadership and empathy for team development
• Clear and persuasive communication with senior stakeholders
• Analytical mindset and evidence-driven decision making
• Generative AI design and deployment
• RAG architectures and vector databases
• LLMOps practices

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