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About 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 prioritize high-value AI use cases, design and deliver pragmatic AI-enabled workflow improvements, and act as the engineering bridge between a specific product line, Global Markets AI, and the AI CoE.

Responsibilities

• 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 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.

Requirements

• 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.
• 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.
• 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.
• 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.
• Computer Science, Engineering, Data Science, Mathematics or related degree, or equivalent practical work experience.
• Relevant cloud, data, AI, cyber, risk, agile, or project delivery certifications.
• 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.

Skills

• Generative AI
• Agentic workflows
• Python
• SQL and databases
• APIs and integration
• Cloud architecture
• Data platforms
• AI-assisted engineering
• CI/CD
• Secure development
• Testing and acceptance
• Communication
• AI platforms
• AI governance
• Benefits realisation
• Conduct and surveillance
• Financial crime
• Employee compliance
• Policy and training
• Workforce governance
• Fairness and privacy
• Investigations
• Microsoft Office

Location

• Remote

Work Type

• Full-time

Experience Level

• Mid-level
• Senior-level

Education Level

• Bachelor's Degree
• Master's Degree
• PhD

About the Company

• Mitsubishi UFJ Financial Group (MUFG) is one of the world’s leading financial groups with 150,000 colleagues globally.
• MUFG strives to make a difference for every client, organization, and community it serves.
• The company stands for its values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.
• MUFG's vision is to be the world’s most trusted financial group.
• It is part of MUFG's culture to put people first, listen to new and diverse ideas, and collaborate toward greater innovation, speed, and agility.
• MUFG invests in talent, technologies, and tools that empower employees to own their careers.
• 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.

Equal Opportunity

• MUFG is committed to embracing diversity and building an inclusive culture where all employees are valued, respected, and their opinions count.
• MUFG supports the principles of equality, diversity, and inclusion in recruitment and employment, and opposes 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.
• MUFG makes recruitment decisions in a non-discriminatory manner in accordance with its commitment to identifying the right skills for the right role and its obligations under the law.