MUFG
Senior AI Engineer - Centre of Excellence, Director
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Discover Your Opportunity with Mitsubishi UFJ Financial Group (MUFG)
Do you want your voice heard and your actions to count? Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world’s leading financial groups. Across the globe, we’re 150,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.
With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career.
Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.
About the Role
GMEO (Global Markets Engineering Office) provides engineering capability, delivery discipline and scalable technology enablement for Global Markets. Global Markets AI is the specialist team responsible for AI strategy, engineering standards, reusable delivery patterns and responsible AI adoption across Global Markets.
The Global Markets AI Centre of Excellence provides the platform, standards, controls and enablement model that allow applied AI adoption to scale safely across Global Markets. Its role is to move Global Markets from AI experimentation to a governed operating model where applied AI becomes part of how every function works, controls, documents and improves.
The Centre of Excellence owns the enterprise AI delivery framework for Global Markets, including AIQ platform capability, enterprise AI architecture, reusable components, model and prompt governance, release controls, AI inventory, training, certification and delivery standards. The CoE does not build every use case. It enables scale, certifies builders, governs release, maintains standards and directly delivers enterprise-grade or high-risk AI capabilities.
Main Purpose of the Role
The GMEO AI Engineer - Centre of Excellence is a Lane 3 role responsible for designing, governing and delivering enterprise-grade AI capability for Global Markets AI Lane 3 covers high-risk, cross-function, model-risk, regulatory-impact or mission-critical AI capabilities that require full enterprise controls, architecture review, security review, release governance and lifecycle management.
The role will help establish AIQ as the governed route from business idea to operationalised IT, supporting intake, risk classification, lane routing, build, test, validation, release, hosting, monitoring and lifecycle management for AI-enabled solutions.
The role will define and maintain the technical standards, patterns, guardrails and reusable components that enable Lane 1 citizen-led AI tools and Lane 2 function-built AI tools to scale safely, while directly engineering Lane 3 enterprise capabilities where complexity, risk or cross-function dependency requires CoE ownership.
The role will support the strategic objective of improving Net Operating Profit through the ABC Plan for AI: Alpha revenue enablement, Beta/Budget cost reduction and Controls-led governance, resilience and evidence automation.
Key Responsibilities
- Lead the engineering design and delivery of Lane 3 enterprise AI capabilities for Global Markets AI, ensuring solutions meet full enterprise architecture, security, governance and operational standards.
- Define, maintain and continuously improve AIQ engineering standards, reusable patterns, control requirements and reference architectures for Global Markets AI adoption.
- Own or support technical governance for AI intake, risk classification, lane routing, architecture review, threat modelling, model and prompt evaluation, release approval and production monitoring.
- Build reusable AI platform components, including RAG frameworks, agentic workflow patterns, connector patterns, evaluation harnesses, logging, audit trails, evidence packs and deployment templates.
- Partner with Lane 2 Function Aligned AI Engineers to review designs, unblock engineering issues, promote reuse and ensure local function-built solutions remain aligned to CoE standards.
- Enable Lane 1 citizen-led delivery by providing safe templates, guardrails, training, certification, review processes and controlled runtime patterns.
- Maintain AI inventory quality and ensure production AI solutions have clear ownership, documentation, data lineage, access control, monitoring, support model and lifecycle status.
- Design and govern AIQ integrations with enterprise tools and data sources, including entitlement controls, DLP, audit logging, read-only execution constraints and operational resilience requirements.
- Work with Technology, Cyber, Risk, Compliance, Legal, Finance, Operations and Front Office stakeholders to ensure AI adoption is safe, supportable, auditable and aligned to Bank and Securities entity obligations.
- Ensure AI-enabled solutions include human oversight, explainability, validation, testing, exception handling, evidence capture and escalation paths proportionate to risk.
- Track and evidence benefits from CoE platform and Lane 3 delivery, including NOP contribution, productivity uplift, cost reduction, control improvement, cycle-time reduction and operational resilience.
- Maintain awareness of emerging AI, agentic engineering, model evaluation, governance and enterprise platform capabilities, assessing their relevance to Global Markets in a controlled and commercially practical manner.
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Work Experience
Essential:
- Enterprise AI delivery – Significant experience designing, building or governing AI, generative AI, agentic AI, automation or data-driven platforms in a regulated enterprise environment.
- Platform engineering – Experience delivering reusable platform capabilities, shared engineering frameworks, enterprise components or developer enablement tooling.
- Regulated controls – Experience working with governance, model risk, compliance, information security, data privacy, audit, operational resilience or control requirements in financial services or another regulated industry.
- Software engineering – Strong experience with modern software engineering practices, including architecture, version control, CI/CD, testing, peer review, security scanning, release management and production support.
- Stakeholder leadership – Experience working with senior business, technology, risk, compliance and control stakeholders to deliver measurable outcomes under policy, control and delivery constraints.
- Cross-functional delivery – Experience delivering capabilities that span multiple functions, systems, data domains, control owners or legal entities.
Preferred:
- Financial markets – Experience in Global Markets, investment banking, capital markets, trading, sales, risk, finance, operations or securities processing environments.
- AI governance – Experience with AI governance frameworks, model inventory, model risk management, prompt/model evaluation, responsible AI, AI risk classification or regulatory expectations for AI.
- Enterprise data – Experience with governed enterprise data platforms, data catalogues, query gateways, entitlement controls, data lineage and audit logging.
- Connector ecosystems – Experience designing secure integrations with enterprise systems such as JIRA, Azure DevOps, OpenPages/GRC, ServiceNow, SharePoint, Confluence, Git, IAM, SIEM or observability platforms.
- Developer enablement – Experience training, certifying or enabling distributed engineering communities, citizen developers or function-aligned delivery teams.
- Production AI operations – Experience with MLOps, LLMOps, monitoring, evaluation, drift detection, incident management, change control and lifecycle management for AI-enabled applications.
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Skills and Experience
Functional / Technical Competencies:
Essential
- Generative AI architecture – Deep understanding of generative AI concepts, including prompt design, model selection, RAG, embeddings, vector search, tool use, agentic orchestration, evaluation, hallucination risk and guardrails.
- Enterprise controls – Ability to design AI solutions with appropriate access control, audit logging, data classification, DLP, secrets management, human oversight, exception handling, monitoring and evidence capture.
- Python and engineering – Strong Python development skills and ability to build maintainable, tested, documented and supportable enterprise code.
- Platform design – Ability to design reusable frameworks, platform services, APIs, SDKs, templates and reference implementations for distributed delivery teams.
- Cloud architecture – Strong understanding of cloud-based programming and architecture, particularly Azure; AWS experience is also beneficial.
- Data platforms – SQL and database experience, including enterprise data access, transformation, validation, data lineage and integration with platforms such as Snowflake, Starburst or equivalent.
- CI/CD and release – Strong use of industry-standard CI/CD and software delivery tools such as Git, TeamCity, deployment automation, issue tracking and release governance.
- AI-assisted engineering – Advanced use of AI-assisted engineering tools such as GitHub Copilot, Claude Code or equivalent agentic coding harnesses, with appropriate review, testing and control of generated outputs.
- Testing and validation – Ability to define test strategies for AI-enabled solutions, including functional testing, regression testing, prompt/model evaluation, adversarial testing, UAT, control testing and release evidence.
- Architecture communication – Ability to communicate complex technical, governance and risk concepts clearly to senior stakeholders and convert strategic objectives into practical delivery patterns.
Preferred:
- MLOps and LLMOps – Experience with model lifecycle management, model/prompt versioning, evaluation harnesses, monitoring and AI operational support.
- Connector fabric – Experience with secure connector design, MCP-style gateways, API gateways, entitlement enforcement, query policy, DLP, logging and bounded execution patterns.
- Governance tooling – Familiarity with GRC tooling, control libraries, evidence packs, risk acceptance, issue management and audit workflows.
- Financial services regulation – Familiarity with AI governance, model risk, operational resilience, EU AI Act concepts, data privacy and regulatory expectations for AI in financial services.
- Value measurement – Understanding of benefits realisation, baselining, KPI definition, NOP improvement, cost reduction, control improvement and post-implementation measurement.
Education / Qualifications:
Essential
- Computer Science, Engineering, Data Science, Mathematics or related degree, or equivalent practical work experience.
Preferred:
- Relevant cloud, data, AI, cyber, risk, architecture, agile or project delivery certifications.
Personal Requirements
- Strategic engineering judgement – Ability to balance speed, innovation, governance, security, resilience and commercial value in a regulated banking environment.
- Communication – Excellent communication skills, including the ability to engage senior stakeholders and explain AI architecture
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