AI Engineer, VP
On-siteLondon, England, United Kingdom
London, England, United KingdomOn-siteFull TimeSenior LevelEnterprise
Full TimeSenior LevelEnterprise
Job Summary
Prioritize and deliver AI opportunities within specific product lines, focusing on measurable productivity, quality, risk, control, and service outcomes. Partner with leaders and process owners to assess workflows, quantify benefits, and define success measures for generative AI, agentic workflows, and retrieval-augmented generation solutions. Build, configure, and integrate AI-enabled workflow improvements while ensuring compliance with MUFG's AI governance, model risk, and regulatory requirements. Design controls into AI processes, including human-in-the-loop review, explainability, and audit trails, acting as the engineering bridge between product lines and the AI Centre of Excellence. Deliver rapid prototypes and production solutions that meet engineering and control expectations, tracking benefits such as run-rate savings and cycle-time reduction. Provide training and documentation to users, ensuring responsible adoption across Bank and Securities entities.
Required Qualifications
- AI and automation delivery – Experience designing, building or implementing AI, generative AI, automation, analytics or data-driven workflow solutions in a corporate or financial services environment
- Solution delivery – Practical experience translating business requirements into engineered solutions, including process analysis, solution design, build, testing, deployment and adoption support
- Regulated controls – Experience working with governance, risk, compliance, information security, data privacy or control requirements in a regulated environment
- Engineering practice – Experience using modern software engineering practices, including version control, CI/CD, testing, documentation, peer review and release management
- Stakeholder delivery – Experience working with business stakeholders and technology teams to deliver measurable outcomes under time, budget, policy and control constraints
- Computer Science, Engineering, Data Science, Mathematics or related degree, or equivalent practical work experience
- Strong understanding of generative AI concepts, including prompt design, model selection, RAG, embeddings, evaluation, hallucination risk, guardrails and responsible AI controls
- Ability to design and implement agentic or semi-agentic workflows with appropriate human oversight, logging, validation and exception handling
- Strong Python development skills and ability to build maintainable, tested and documented code
- SQL and database experience, including data extraction, transformation, validation and integration
- Experience with APIs, workflow integration and secure system-to-system connectivity
- Practical understanding of cloud-based programming and architecture, particularly Azure
- Experience with enterprise data platforms such as Snowflake or equivalent
- Use of AI-assisted engineering tools such as GitHub Copilot, Claude Code or equivalent agentic coding harnesses, with appropriate review and control of generated outputs
- Use of industry-standard CI/CD and software delivery tools such as Git, TeamCity, deployment automation and issue-tracking platforms
- Understanding of secure software development, data classification, access control, secrets management and auditability
- Ability to define test plans and acceptance criteria for AI-enabled solutions, including functional testing, regression testing, model and prompt evaluation, and control testing
- Excellent communication skills, including the ability to engage senior stakeholders and explain AI risks and opportunities clearly
- Results driven, with a strong sense of accountability and ownership
- Proactive and motivated, with the ability to identify opportunities and drive them through to delivery
- Ability to operate with urgency and prioritise work according to business value, risk and delivery constraints
- Strong decision-making skills and sound judgement, especially where AI outputs affect controls, decisions or regulated processes
- Structured and logical approach to problem solving
- Creative and innovative mindset, balanced with strong risk awareness and control discipline
- Excellent interpersonal skills and ability to work across functions, technology, risk, compliance and governance teams
- Ability to manage large workloads and tight deadlines
- Excellent attention to detail and accuracy
- Calm approach, with the ability to perform well in a pressurised environment
- Strong numerical and analytical skills
- Strong Microsoft Office skills and ability to produce clear documentation, presentations and process materials
Desired Qualifications
- Experience implementing generative AI or agentic AI solutions in enterprise environments
- Experience with retrieval-augmented generation, vector search, knowledge management, document intelligence or workflow orchestration
- Experience in banking, capital markets, financial services or another highly regulated industry
- Experience with cloud platforms, data platforms and enterprise integration patterns
- Experience with AI orchestration frameworks, model evaluation tooling, vector databases, document processing, knowledge retrieval or workflow automation platforms
- Familiarity with model risk management, AI governance, EU AI Act concepts, data privacy and regulatory expectations for AI in financial services
- Understanding of process improvement methods and benefits realisation, including baselining, KPI definition and post-implementation measurement
- Conduct risk, market abuse, surveillance and communications monitoring concepts
- AML, sanctions, KYC and transaction monitoring control concepts
- Personal account dealing, outside interests, gifts, entertainment and conflicts
- Regulatory training, attestations, policy management and evidence tracking
- Workforce analytics, skills data and compensation governance considerations
- Data privacy, employment law sensitivity, fairness and bias risk in AI models
- Investigation workflows, case management and defensible audit trails
- Relevant cloud, data, AI, cyber, risk, agile or project delivery certifications
- AWS experience
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