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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 AI Centre of Excellence (AI CoE) defines the enterprise framework, standards, reusable patterns, controls and delivery practices for the responsible adoption of AI across MUFG. Function Aligned AI Engineers are embedded into a specific business or support function to translate functional priorities into safe, practical and measurable AI-enabled outcomes while remaining aligned to Global Markets AI and AI CoE standards.
The role holder will work closely with process owners, risk and control stakeholders, technology teams, data owners, Global Markets AI and the AI CoE to identify, design, build and embed AI solutions that improve productivity, control effectiveness, employee and conduct governance, decision support and operational resilience.
The role holder will work together to support the control environment, conduct framework, workforce governance and regulatory expectations for employees operating across Bank and Securities entities. Compliance typically oversees conduct risk, financial crime, market abuse surveillance, communications monitoring, conflicts, personal account dealing, client classification, regulatory reporting oversight, training and supervisory engagement.
MAIN PURPOSE OF THE ROLE
The Function Aligned AI Engineer - is responsible for accelerating responsible AI adoption while ensuring alignment with MUFG's AI policy, data governance, technology standards, control framework, Global Markets AI engineering standards and risk appetite.
The role will identify and prioritise high-value AI use cases, with clear linkage to control effectiveness, conduct governance, employee lifecycle efficiency, productivity, service improvement or risk reduction.
The role will design and deliver pragmatic AI-enabled workflow improvements, including generative AI, agentic workflows, retrieval-augmented generation, process automation, analytics and decision-support solutions where appropriate.
The role will act as the engineering bridge between a specific product line, Global Markets AI and the AI CoE, ensuring local solutions reuse approved platforms, patterns, models, controls and delivery standards.
The role will embed AI solutions into day-to-day processes with appropriate human oversight, auditability, monitoring, evidence capture and benefits tracking.
KEY RESPONSIBILITIES
Prioritise 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 current workflows, identify pain points, quantify benefits, define success measures and create practical delivery roadmaps.
Build, configure and integrate AI solutions using approved enterprise platforms, tools and patterns, including generative AI, agentic workflows, RAG, prompt orchestration, workflow automation and data-driven decision support.
Ensure solutions comply with MUFG's AI governance, model risk, information security, data privacy, records management, regulatory, compliance and operational resilience requirements.
Design controls into AI-enabled processes, including human-in-the-loop review, explainability, validation, testing, monitoring, exception handling, evidence capture and audit trails.
Collaborate with Global Markets AI and the AI CoE to reuse common components, contribute reusable patterns and ensure local delivery remains aligned to enterprise AI architecture and engineering standards.
Work with technology, data, cyber, legal, risk, finance and operational teams to obtain required approvals and ensure solutions are supportable, secure and scalable.
Deliver rapid prototypes and quick wins where appropriate, while ensuring production solutions meet engineering, governance and control expectations.
Track benefits and adoption after implementation, including run-rate savings, productivity uplift, quality improvement, cycle-time reduction, risk reduction and user engagement.
Provide training, documentation and practical guidance to users so AI tools are used responsibly, consistently and effectively.
Maintain awareness of emerging AI capabilities and assess their relevance in a controlled and commercially practical manner.
Escalate risks, issues, control gaps or conflicts of priority promptly through the functional reporting line, Global Markets AI and AI CoE governance channels.
WORK EXPERIENCE
Essential:
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.
Preferred:
Enterprise GenAI – Experience implementing generative AI or agentic AI solutions in enterprise environments.
Knowledge workflows – Experience with retrieval-augmented generation, vector search, knowledge management, document intelligence or workflow orchestration.
Regulated industry – Experience in banking, capital markets, financial services or another highly regulated industry.
Platforms – Experience with cloud platforms, data platforms and enterprise integration patterns.
SKILLS AND EXPERIENCE
Essential
Generative AI – Strong understanding of generative AI concepts, including prompt design, model selection, RAG, embeddings, evaluation, hallucination risk, guardrails and responsible AI controls.
Agentic workflows – Ability to design and implement agentic or semi-agentic workflows with appropriate human oversight, logging, validation and exception handling.
Python – Strong Python development skills and ability to build maintainable, tested and documented code.
SQL and databases – SQL and database experience, including data extraction, transformation, validation and integration.
APIs and integration – Experience with APIs, workflow integration and secure system-to-system connectivity.
Cloud architecture – Practical understanding of cloud-based programming and architecture, particularly Azure; AWS experience is also beneficial.
Data platforms – Experience with enterprise data platforms such as Snowflake or equivalent.
AI-assisted engineering – 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.
CI/CD – Use of industry-standard CI/CD and software delivery tools such as Git, TeamCity, deployment automation and issue-tracking platforms.
Secure development – Understanding of secure software development, data classification, access control, secrets management and auditability.
Testing and acceptance – Ability to define test plans and acceptance criteria for AI-enabled solutions, including functional testing, regression testing, model and prompt evaluation, and control testing.
Communication – Ability to communicate technical concepts clearly to non-technical stakeholders and convert functional problems into practical solution designs.
Preferred:
AI platforms – Experience with AI orchestration frameworks, model evaluation tooling, vector databases, document processing, knowledge retrieval or workflow automation platforms.
AI governance – Familiarity with model risk management, AI governance, EU AI Act concepts, data privacy and regulatory expectations for AI in financial services.
Benefits realisation – Understanding of process improvement methods and benefits realisation, including baselining, KPI definition and post-implementation measurement.
Conduct and surveillance – Conduct risk, market abuse, surveillance and communications monitoring concepts.
Financial crime – AML, sanctions, KYC and transaction monitoring control concepts.
Employee compliance – Personal account dealing, outside interests, gifts, entertainment and conflicts.
Policy and training – Regulatory training, attestations, policy management and evidence tracking.
Workforce governance – Workforce analytics, skills data and compensation governance considerations.
Fairness and privacy – Data privacy, employment law sensitivity, fairness and bias risk in AI models.
Investigations – Investigation workflows, case management and defensible audit trails.
Education / Qualifications:
Essential
Computer Science, Engineering, Data Science, Mathematics or related degree, or equivalent practical work experience.
Preferred:
Relevant cloud, data, AI, cyber, risk, agile or project delivery certifications.
PERSONAL REQUIREMENTS
Communication – Excellent communication skills, including the ability to engage senior stakeholders and explain AI risks and opportunities clearly.
Accountability – Results driven, with a strong sense of accountability and ownership.
Proactivity – Proactive and motivated, with the ability to identify opportunities and drive them through to delivery.
Prioritisation – Ability to operate with urgency and prioritise work according to business value, risk and delivery constraints.
Judgement – Strong decision-making skills and sound judgement, especially where AI outputs affect controls, decisions or regulated processes.
Problem solving – Structured and logical approach to problem solving.
Innovation and control – Creative and innovative mindset, balanced with strong risk awareness and control discipline.
Collaboration – Excellent interpersonal skills and ability to work across functions, technology, risk, compliance and governance teams.
Workload management – Ability to manage large workloads and tight deadlines.
Accuracy – Excellent attention to detail and accuracy.
Resilience – Calm approach, with the ability to perform well in a pressurised environment.
Analysis – Strong numerical and analytical skills.
Documentation – Strong Microsoft Office skills and ability to produce clear documentation, presentations and process materials.
We are open to considering flexible working requests in line with organisational requirements.
MUFG is committed to embracing diversity and building an inclusive culture where all employees are valued, respected and their opinions count. We support the principles of equality, diversity and inclusion in recruitment and employment, and oppose all forms of discrimination on the grounds of age, sex, gender, sexual orientation, disability, pregnancy and maternity, race, gender reassignment, religion or belief and marriage or civil partnership.
We make our recruitment decisions in a non-discriminatory manner in accordance with our commitment to identifying the right skills for the right role and our obligations under the law.
