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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. Position Summary This is a Director-level leadership position responsible for building and leading the organisation’s AI and Data engineering capabilities and delivering measurable business value through the integration of technology, data, and business expertise. We are seeking an engineering-led AI builder who combines recent, hands-on technical depth with the ability to lead a global Data and AI organisation at enterprise scale. The successful candidate will have personally designed, built, and shipped production-grade Generative AI solutions within the last two to three years and will remain comfortable working directly with engineering teams, codebases, architecture decisions, and production challenges. The role will lead a combined AI and Data organisation of approximately 100 professionals and will own outcomes end to end, from business problem definition and rapid experimentation through engineering, implementation, adoption, and production operations. This leader will be expected to: Build practical, production-grade AI and Data products that create measurable business value. Set the technical direction and engineering standards for the AI and Data organisation. Attract, identify, inspire, and develop exceptional engineering talent. Create a culture of technical excitement, craftsmanship, experimentation, and continuous learning. Partner with global stakeholders to shape and execute enterprise AI and Data strategies. Deliver innovation with the governance, security, traceability, resilience, and risk management expected within a global financial institution. The ideal candidate is not only a programme sponsor or portfolio leader. They are a credible and current AI engineering practitioner who can challenge technical decisions, help solve difficult engineering problems, and energise teams through personal technical leadership. Roles and Responsibilities 1. Hands-on AI Product and Engineering Leadership Take end-to-end ownership of AI and Data products, from opportunity identification, architecture, and prototyping through engineering, deployment, adoption, and production operations. Personally contribute to the technical direction of major initiatives, including solution architecture, design reviews, engineering trade-offs, model and platform selection, and production readiness. Remain sufficiently hands-on to: Whiteboard and design an end-to-end AI solution. Review architecture, code, and pull requests where appropriate. Diagnose retrieval, orchestration, data quality, performance, and model behaviour issues. Challenge engineering assumptions and design choices with technical credibility. Support teams directly when resolving complex delivery or production problems. Translate complex business challenges into high-value AI and Data use cases with measurable outcomes, clear adoption plans, and appropriate operational controls. Lead the practical application of modern AI engineering patterns, including: Retrieval-Augmented Generation, or RAG. Knowledge graphs and context graphs. Agentic architectures and multi-agent systems. Tool-use and skill-based frameworks. Model Context Protocol, or MCP, and similar integration standards. Model evaluation, observability, monitoring, and guardrails. Human-in-the-loop workflows. Secure integration of enterprise data, applications, models, tools, and services. Establish robust approaches for evaluating Generative AI solutions, including output quality, grounding, retrieval effectiveness, reliability, latency, cost, security, explainability, and business impact. Ensure prototypes can progress into secure, scalable, reusable, and supportable production solutions rather than remaining isolated experiments. Provide technical leadership across AI\u002FML, Generative AI, data engineering, analytics, data platforms, and software engineering. 2. Data Platform and Architecture Leadership Lead the design, development, and evolution of enterprise Data and AI platforms that enable secure and scalable delivery across global business functions. Drive modern data architecture and engineering practices across: ETL and ELT. Data warehouses. Data lakes and lakehouse architectures. Data marts. Data virtualisation. Metadata and lineage. Real-time and batch data processing. APIs and reusable data services. Data and AI integration patterns. Ensure platforms support both traditional analytics and modern AI workloads, including governed access to structured and unstructured enterprise information. Set clear standards for architecture quality, modularity, reusability, maintainability, interoperability, performance, and operational resilience. Partner with cybersecurity, architecture, infrastructure, risk, compliance, legal, privacy, and data governance teams to embed controls into technology design from the outset. Balance strategic platform investment with rapid use-case delivery, avoiding unnecessary complexity while building reusable enterprise capabilities. 3. Engineering Excellence and Innovation Culture Establish and sustain a culture of technical excitement, engineering craftsmanship, curiosity, and disciplined experimentation across the AI and Data organisation. Drive innovation from the front by introducing relevant engineering patterns, tools, and techniques and by participating directly in experimentation and technical reviews. Create practical mechanisms for engineers to explore and apply emerging technologies, including: Rapid prototyping. Engineering hackathons. Internal technical talks and demonstrations. Architecture and code review forums. Technical communities of practice. Reusable reference implementations. Build-and-learn initiatives. Structured experimentation with clear success and stop criteria. Act as a bar-raiser for engineering excellence, setting high standards for: Technical design quality. Code quality and maintainability. Reusability and common engineering patterns. Testing and evaluation. Documentation and traceability. Security and resilience. Responsible AI. Production readiness and operational ownership. Create an environment where teams can move quickly while maintaining appropriate engineering, risk, and governance discipline. Convert promising emerging technologies into practical, governed, and scalable solutions that address real business needs. Encourage teams to challenge assumptions, learn from unsuccessful experiments, share knowledge openly, and continuously improve engineering practices. 4. Talent Magnet and Technical Capability Builder Attract and retain high-calibre AI, data, and software engineering talent through the leader’s technical credibility, reputation, vision, and ability to create meaningful engineering opportunities. Demonstrate a strong record of identifying exceptional engineers and building focused, high-performing teams capable of delivering disproportionate impact. Personally participate in the assessment and selection of critical technical hires, particularly senior individual contributors, architects, engineering leads, data scientists, and AI specialists. Establish a rigorous technical hiring bar and ensure that recruitment assesses practical engineering capability, problem-solving, architecture judgement, learning agility, and delivery experience. Build an organisation in which outstanding engineers want to work with the leader and for the mission, rather than being attracted only by position or title. Develop multiple technical career paths, with particular emphasis on growing: Senior individual contributors. Principal and lead engineers. AI and Data architects. Technical product leaders. Engineering managers. Future enterprise technology leaders. Coach and mentor senior technical talent directly, providing challenging assignments, candid technical feedback, and opportunities for global exposure and influence. Build succession depth across both leadership and technical specialist roles. Strengthen the organisation through a thoughtful combination of hiring, internal mobility, upskilling, mentoring, communities of practice, and targeted external partnerships . 5. Global Delivery and Enterprise-Scale Ownership Lead a combined AI and Data organisation of approximately 100 professionals and take accountability for business, technology, talent, risk, and operational outcomes. Own delivery end to end, from use-case selection and requirements definition through design, development, deployment, adoption, production support, and value realisation. Manage multiple concurrent AI, Data, platform, and transformation initiatives across global and cross-functional environments. Set priorities and optimise the portfolio based on business value, strategic alignment, feasibility, risk, reuse potential, organisational readiness, and available capacity. Establish clear accountability for scope, outcomes, quality, schedule, resources, dependencies, risks, adoption, and operational performance. Identify and remove technical, organisational, and decision-making bottlenecks. Ensure that product and engineering teams remain focused on measurable outcomes rather than activity, technology adoption, or delivery milestones alone. Establish transparent success measures covering business value, user adoption, engineering quality, operational reliability, risk, and responsible AI. 6. Global Stakeholder and Strategy Leadership Develop trusted relationships with senior global stakeholders across business, technology, operations, risk, compliance, data, and corporate functions. Understand business priorities and pain points and translate them into practical AI and Data strategies, products, platforms, and delivery roadmaps. Communicate complex technical topics to senior management in clear business language, including available options, trade-offs, costs, risks, dependencies, and expected value. Lead decisions and create alignment across global and cross-functional teams, including situations involving competing priorities or incomplete information. Contribute to the development and execution of global AI, Data, and digital strategies. Optimise capabilities and solutions across regions by encouraging reuse, common standards, shared assets, and effective allocation of specialist resources. Represent the GCC as a credible global centre for AI and Data engineering, innovation, and enterprise delivery. 7. Responsible AI, Data Governance, and Bank-Grade Discipline Deliver innovation within a regulated and risk-aware environment by combining speed and experimentation with appropriate governance and control. Embed Responsible AI principles throughout the product lifecycle, including fairness, transparency, explainability, privacy, security, accountability, and human oversight. Establish controls appropriate to each solution, including: Traceability and auditability. Human-in-the-loop decision points. Data protection and access control. Model and prompt evaluation. Content safety and output controls. Monitoring for quality, drift, misuse, and unexpected behaviour. Escalation, fallback, and remediation mechanisms. Third-party model and technology risk management. Lead the operationalisation of global data management practices, including data definitions, ownership, quality, lineage, metadata, retention, and access control. Ensure consistent implementation of established data governance, security, privacy, compliance, and risk frameworks. Make risk-informed decisions that enable innovation while protecting customers, employees, the organisation, and its stakeholders. 8. Enterprise Transformation and Value Creation Drive enterprise-wide transformation by scaling AI, analytics, automation, and modern data capabilities across business and corporate functions. Build repeatable approaches for identifying, validating, prioritising, and scaling high-value use cases. Promote collaboration and knowledge sharing across global teams through common patterns, reusable assets, reference architectures, and documented best practices. Develop adoption and change strategies so that AI and Data solutions become embedded in business processes and ways of working. Track benefits beyond technical delivery, including adoption, productivity, decision quality, risk reduction, customer or employee impact, and financial value. Ensure that transformation efforts strengthen long-term organisational capability rather than creating isolated technology solutions. Job Requirements Required Qualifications More than 15 years of relevant experience across AI\u002FML, Generative AI, data engineering, software engineering, data platforms, or advanced analytics. At least five years of experience leading significant engineering teams, technical organisations, products, or complex global initiatives. Personal, hands-on experience designing, building, and shipping production-grade AI or Generative AI solutions within the last two to three years. Evidence of direct technical contribution to recent production solutions, rather than experience limited to sponsorship, programme oversight, vendor management, or executive governance. Demonstrated recent, hands-on depth in several of the following: Retrieval-Augmented Generation. Knowledge graphs or context graphs. Agentic architectures and orchestration. Tool-use or skill-based AI frameworks. Model Context Protocol or similar integration standards. Enterprise search and retrieval. LLM evaluation, observability, security, and guardrails. Secure integration of AI models with enterprise data and applications. Ability to participate credibly in architecture reviews, code and design discussions, production troubleshooting, and complex engineering decisions. Proven experience taking AI or Data products from business problem definition through architecture, engineering, deployment, adoption, and ongoing production operations. Strong record of attracting, assessing, hiring, and retaining high-calibre engineering talent. Demonstrated success developing senior individual contributors, technical leads, architects, and engineering managers. Experience establishing and maintaining high engineering standards across design, implementation, testing, evaluation, security, reliability, and operations. Proven experience leading large-scale or complex teams in a global environment. Experience managing multiple concurrent products, programmes, and technical initiatives while maintaining clarity of priorities and accountability. Strong stakeholder management experience across global, senior, and cross-functional environments. Ability to navigate ambiguity, make difficult decisions, challenge constructively, and drive both organisational and technical progress. Strong communication and influencing skills, including the ability to explain complex technology choices and trade-offs to senior management. Demonstrated commitment to Responsible AI, secure engineering, data governance, and risk management. Preferred Qualifications Experience leading a combined AI and Data organisation of significant scale. Experience establishing or operating a global AI\u002FData Centre of Excellence, engineering hub, product organisation, or similar capability. Deep experience designing and operating enterprise data platforms, including data warehouses, data lakes or lakehouses, data marts, data virtualisation, and data integration services. Experience implementing AI and Data solutions in a financial institution, regulated industry, or similarly complex risk environment. Experience with model governance, AI risk management, privacy, security, traceability, and human-in-the-loop controls. Experience developing reusable AI platforms, reference architectures, engineering frameworks, or shared services used across multiple business areas. Demonstrated success creating an innovation culture through rapid prototyping, technical communities, internal engineering events, and structured experimentation. Experience contributing to or executing a global AI, Data, technology, or digital transformation strategy. Proven ability to bridge business and technology and to connect technical investment with measurable business outcomes. Experience engaging with external technology partners, universities, research communities, start-ups, or open-source ecosystems. Master’s degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related discipline. A PhD is advantageous but not essential where the candidate demonstrates exceptional practical engineering achievement. Candidate Differentiators The strongest candidates will demonstrate that they: Still build, not only supervise. Have shipped recent AI systems that are actively used in production. Can move confidently between code, architecture, product, people, and executive discussions. Are recognised by engineers as technically credible and inspiring. Have built small, high-calibre teams that achieved impact beyond their size. Develop technical specialists and senior individual contributors, not only people managers. Create energy and excitement around engineering while maintaining delivery discipline. Can turn emerging AI patterns into practical enterprise capabilities. Know how to challenge unnecessary complexity and focus teams on business value. Can combine start-up-style experimentation with bank-grade governance, security, and operational resilience. Equal Opportunity Employer The MUFG Group is committed to providing equal employment opportunities to all applicants and employees and does not discriminate on the basis of race, color, national origin, physical appearance, religion, gender expression, gender identity, sex, age, ancestry, marital status, disability, medical condition, sexual orientation, genetic information, or any other protected status of an individual or that individual's associates or relatives, or any other classification protected by the applicable laws. Mitsubishi UFJ Financial Group (MUFG) is an equal opportunity employer. We view our employees as our key assets as they are fundamental to our long-term growth and success. MUFG is committed to hiring based on merit and organsational fit, regardless of race, religion or gender. At MUFG, our colleagues are our greatest assets. Our Culture Principles provide a roadmap for how each of our colleagues must think and act to become more client-obsessed, inclusive and innovative. They reflect who we are, who we want to be and what we expect from one another. We are excited to see you take the next step in exploring a career with us and encourage you to spend more time reviewing them! Our Culture Principles Client Centric People Focused Listen Up. Speak Up. 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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.\n\n## Position Summary\n\nThis is a Director-level leadership position responsible for building and leading the organisation’s AI and Data engineering capabilities and delivering measurable business value through the integration of technology, data, and business expertise. We are seeking an engineering-led AI builder who combines recent, hands-on technical depth with the ability to lead a global Data and AI organisation at enterprise scale. The successful candidate will have personally designed, built, and shipped production-grade Generative AI solutions within the last two to three years and will remain comfortable working directly with engineering teams, codebases, architecture decisions, and production challenges. The role will lead a combined AI and Data organisation of approximately 100 professionals and will own outcomes end to end, from business problem definition and rapid experimentation through engineering, implementation, adoption, and production operations.\n\nThis leader will be expected to:\n\n- Build practical, production-grade AI and Data products that create measurable business value.\n- Set the technical direction and engineering standards for the AI and Data organisation.\n- Attract, identify, inspire, and develop exceptional engineering talent.\n- Create a culture of technical excitement, craftsmanship, experimentation, and continuous learning.\n- Partner with global stakeholders to shape and execute enterprise AI and Data strategies.\n- Deliver innovation with the governance, security, traceability, resilience, and risk management expected within a global financial institution.\n\nThe ideal candidate is not only a programme sponsor or portfolio leader. They are a credible and current AI engineering practitioner who can challenge technical decisions, help solve difficult engineering problems, and energise teams through personal technical leadership.\n\n## Roles and Responsibilities\n\n### 1. Hands-on AI Product and Engineering Leadership\n\nTake end-to-end ownership of AI and Data products, from opportunity identification, architecture, and prototyping through engineering, deployment, adoption, and production operations.\n\nPersonally contribute to the technical direction of major initiatives, including solution architecture, design reviews, engineering trade-offs, model and platform selection, and production readiness.\n\nRemain sufficiently hands-on to:\n\n- Whiteboard and design an end-to-end AI solution.\n- Review architecture, code, and pull requests where appropriate.\n- Diagnose retrieval, orchestration, data quality, performance, and model behaviour issues.\n- Challenge engineering assumptions and design choices with technical credibility.\n- Support teams directly when resolving complex delivery or production problems.\n\nTranslate complex business challenges into high-value AI and Data use cases with measurable outcomes, clear adoption plans, and appropriate operational controls.\n\nLead the practical application of modern AI engineering patterns, including:\n\n- Retrieval-Augmented Generation, or RAG.\n- Knowledge graphs and context graphs.\n- Agentic architectures and multi-agent systems.\n- Tool-use and skill-based frameworks.\n- Model Context Protocol, or MCP, and similar integration standards.\n- Model evaluation, observability, monitoring, and guardrails.\n- Human-in-the-loop workflows.\n- Secure integration of enterprise data, applications, models, tools, and services.\n\nEstablish robust approaches for evaluating Generative AI solutions, including output quality, grounding, retrieval effectiveness, reliability, latency, cost, security, explainability, and business impact.\n\nEnsure prototypes can progress into secure, scalable, reusable, and supportable production solutions rather than remaining isolated experiments.\n\nProvide technical leadership across AI\u002FML, Generative AI, data engineering, analytics, data platforms, and software engineering.\n\n### 2. Data Platform and Architecture Leadership\n\nLead the design, development, and evolution of enterprise Data and AI platforms that enable secure and scalable delivery across global business functions.\n\nDrive modern data architecture and engineering practices across:\n\n- ETL and ELT.\n- Data warehouses.\n- Data lakes and lakehouse architectures.\n- Data marts.\n- Data virtualisation.\n- Metadata and lineage.\n- Real-time and batch data processing.\n- APIs and reusable data services.\n- Data and AI integration patterns.\n\nEnsure platforms support both traditional analytics and modern AI workloads, including governed access to structured and unstructured enterprise information.\n\nSet clear standards for architecture quality, modularity, reusability, maintainability, interoperability, performance, and operational resilience.\n\nPartner with cybersecurity, architecture, infrastructure, risk, compliance, legal, privacy, and data governance teams to embed controls into technology design from the outset.\n\nBalance strategic platform investment with rapid use-case delivery, avoiding unnecessary complexity while building reusable enterprise capabilities.\n\n### 3. Engineering Excellence and Innovation Culture\n\nEstablish and sustain a culture of technical excitement, engineering craftsmanship, curiosity, and disciplined experimentation across the AI and Data organisation.\n\nDrive innovation from the front by introducing relevant engineering patterns, tools, and techniques and by participating directly in experimentation and technical reviews.\n\nCreate practical mechanisms for engineers to explore and apply emerging technologies, including:\n\n- Rapid prototyping.\n- Engineering hackathons.\n- Internal technical talks and demonstrations.\n- Architecture and code review forums.\n- Technical communities of practice.\n- Reusable reference implementations.\n- Build-and-learn initiatives.\n- Structured experimentation with clear success and stop criteria.\n\nAct as a bar-raiser for engineering excellence, setting high standards for:\n\n- Technical design quality.\n- Code quality and maintainability.\n- Reusability and common engineering patterns.\n- Testing and evaluation.\n- Documentation and traceability.\n- Security and resilience.\n- Responsible AI.\n- Production readiness and operational ownership.\n\nCreate an environment where teams can move quickly while maintaining appropriate engineering, risk, and governance discipline.\n\nConvert promising emerging technologies into practical, governed, and scalable solutions that address real business needs.\n\nEncourage teams to challenge assumptions, learn from unsuccessful experiments, share knowledge openly, and continuously improve engineering practices.\n\n### 4. Talent Magnet and Technical Capability Builder\n\nAttract and retain high-calibre AI, data, and software engineering talent through the leader’s technical credibility, reputation, vision, and ability to create meaningful engineering opportunities.\n\nDemonstrate a strong record of identifying exceptional engineers and building focused, high-performing teams capable of delivering disproportionate impact.\n\nPersonally participate in the assessment and selection of critical technical hires, particularly senior individual contributors, architects, engineering leads, data scientists, and AI specialists.\n\nEstablish a rigorous technical hiring bar and ensure that recruitment assesses practical engineering capability, problem-solving, architecture judgement, learning agility, and delivery experience.\n\nBuild an organisation in which outstanding engineers want to work with the leader and for the mission, rather than being attracted only by position or title.\n\nDevelop multiple technical career paths, with particular emphasis on growing:\n\n- Senior individual contributors.\n- Principal and lead engineers.\n- AI and Data architects.\n- Technical product leaders.\n- Engineering managers.\n- Future enterprise technology leaders.\n\nCoach and mentor senior technical talent directly, providing challenging assignments, candid technical feedback, and opportunities for global exposure and influence.\n\nBuild succession depth across both leadership and technical specialist roles.\n\nStrengthen the organisation through a thoughtful combination of hiring, internal mobility, upskilling, mentoring, communities of practice, and targeted external partnerships .\n\n### 5. Global Delivery and Enterprise-Scale Ownership\n\nLead a combined AI and Data organisation of approximately 100 professionals and take accountability for business, technology, talent, risk, and operational outcomes.\n\nOwn delivery end to end, from use-case selection and requirements definition through design, development, deployment, adoption, production support, and value realisation.\n\nManage multiple concurrent AI, Data, platform, and transformation initiatives across global and cross-functional environments.\n\nSet priorities and optimise the portfolio based on business value, strategic alignment, feasibility, risk, reuse potential, organisational readiness, and available capacity.\n\nEstablish clear accountability for scope, outcomes, quality, schedule, resources, dependencies, risks, adoption, and operational performance.\n\nIdentify and remove technical, organisational, and decision-making bottlenecks.\n\nEnsure that product and engineering teams remain focused on measurable outcomes rather than activity, technology adoption, or delivery milestones alone.\n\nEstablish transparent success measures covering business value, user adoption, engineering quality, operational reliability, risk, and responsible AI.\n\n### 6. Global Stakeholder and Strategy Leadership\n\nDevelop trusted relationships with senior global stakeholders across business, technology, operations, risk, compliance, data, and corporate functions.\n\nUnderstand business priorities and pain points and translate them into practical AI and Data strategies, products, platforms, and delivery roadmaps.\n\nCommunicate complex technical topics to senior management in clear business language, including available options, trade-offs, costs, risks, dependencies, and expected value.\n\nLead decisions and create alignment across global and cross-functional teams, including situations involving competing priorities or incomplete information.\n\nContribute to the development and execution of global AI, Data, and digital strategies.\n\nOptimise capabilities and solutions across regions by encouraging reuse, common standards, shared assets, and effective allocation of specialist resources.\n\nRepresent the GCC as a credible global centre for AI and Data engineering, innovation, and enterprise delivery.\n\n### 7. Responsible AI, Data Governance, and Bank-Grade Discipline\n\nDeliver innovation within a regulated and risk-aware environment by combining speed and experimentation with appropriate governance and control.\n\nEmbed Responsible AI principles throughout the product lifecycle, including fairness, transparency, explainability, privacy, security, accountability, and human oversight.\n\nEstablish controls appropriate to each solution, including:\n\n- Traceability and auditability.\n- Human-in-the-loop decision points.\n- Data protection and access control.\n- Model and prompt evaluation.\n- Content safety and output controls.\n- Monitoring for quality, drift, misuse, and unexpected behaviour.\n- Escalation, fallback, and remediation mechanisms.\n- Third-party model and technology risk management.\n\nLead the operationalisation of global data management practices, including data definitions, ownership, quality, lineage, metadata, retention, and access control.\n\nEnsure consistent implementation of established data governance, security, privacy, compliance, and risk frameworks.\n\nMake risk-informed decisions that enable innovation while protecting customers, employees, the organisation, and its stakeholders.\n\n### 8. Enterprise Transformation and Value Creation\n\nDrive enterprise-wide transformation by scaling AI, analytics, automation, and modern data capabilities across business and corporate functions.\n\nBuild repeatable approaches for identifying, validating, prioritising, and scaling high-value use cases.\n\nPromote collaboration and knowledge sharing across global teams through common patterns, reusable assets, reference architectures, and documented best practices.\n\nDevelop adoption and change strategies so that AI and Data solutions become embedded in business processes and ways of working.\n\nTrack benefits beyond technical delivery, including adoption, productivity, decision quality, risk reduction, customer or employee impact, and financial value.\n\nEnsure that transformation efforts strengthen long-term organisational capability rather than creating isolated technology solutions.\n\n## Job Requirements\n\n### Required Qualifications\n\nMore than 15 years of relevant experience across AI\u002FML, Generative AI, data engineering, software engineering, data platforms, or advanced analytics.\n\nAt least five years of experience leading significant engineering teams, technical organisations, products, or complex global initiatives.\n\nPersonal, hands-on experience designing, building, and shipping production-grade AI or Generative AI solutions within the last two to three years.\n\nEvidence of direct technical contribution to recent production solutions, rather than experience limited to sponsorship, programme oversight, vendor management, or executive governance.\n\nDemonstrated recent, hands-on depth in several of the following:\n\n- Retrieval-Augmented Generation.\n- Knowledge graphs or context graphs.\n- Agentic architectures and orchestration.\n- Tool-use or skill-based AI frameworks.\n- Model Context Protocol or similar integration standards.\n- Enterprise search and retrieval.\n- LLM evaluation, observability, security, and guardrails.\n- Secure integration of AI models with enterprise data and applications.\n\nAbility to participate credibly in architecture reviews, code and design discussions, production troubleshooting, and complex engineering decisions.\n\nProven experience taking AI or Data products from business problem definition through architecture, engineering, deployment, adoption, and ongoing production operations.\n\nStrong record of attracting, assessing, hiring, and retaining high-calibre engineering talent.\n\nDemonstrated success developing senior individual contributors, technical leads, architects, and engineering managers.\n\nExperience establishing and maintaining high engineering standards across design, implementation, testing, evaluation, security, reliability, and operations.\n\nProven experience leading large-scale or complex teams in a global environment.\n\nExperience managing multiple concurrent products, programmes, and technical initiatives while maintaining clarity of priorities and accountability.\n\nStrong stakeholder management experience across global, senior, and cross-functional environments.\n\nAbility to navigate ambiguity, make difficult decisions, challenge constructively, and drive both organisational and technical progress.\n\nStrong communication and influencing skills, including the ability to explain complex technology choices and trade-offs to senior management.\n\nDemonstrated commitment to Responsible AI, secure engineering, data governance, and risk management.\n\n### Preferred Qualifications\n\nExperience leading a combined AI and Data organisation of significant scale.\n\nExperience establishing or operating a global AI\u002FData Centre of Excellence, engineering hub, product organisation, or similar capability.\n\nDeep experience designing and operating enterprise data platforms, including data warehouses, data lakes or lakehouses, data marts, data virtualisation, and data integration services.\n\nExperience implementing AI and Data solutions in a financial institution, regulated industry, or similarly complex risk environment.\n\nExperience with model governance, AI risk management, privacy, security, traceability, and human-in-the-loop controls.\n\nExperience developing reusable AI platforms, reference architectures, engineering frameworks, or shared services used across multiple business areas.\n\nDemonstrated success creating an innovation culture through rapid prototyping, technical communities, internal engineering events, and structured experimentation.\n\nExperience contributing to or executing a global AI, Data, technology, or digital transformation strategy.\n\nProven ability to bridge business and technology and to connect technical investment with measurable business outcomes.\n\nExperience engaging with external technology partners, universities, research communities, start-ups, or open-source ecosystems.\n\nMaster’s degree or higher in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related discipline. A PhD is advantageous but not essential where the candidate demonstrates exceptional practical engineering achievement.\n\n### Candidate Differentiators\n\nThe strongest candidates will demonstrate that they:\n\n- Still build, not only supervise.\n- Have shipped recent AI systems that are actively used in production.\n- Can move confidently between code, architecture, product, people, and executive discussions.\n- Are recognised by engineers as technically credible and inspiring.\n- Have built small, high-calibre teams that achieved impact beyond their size.\n- Develop technical specialists and senior individual contributors, not only people managers.\n- Create energy and excitement around engineering while maintaining delivery discipline.\n- Can turn emerging AI patterns into practical enterprise capabilities.\n- Know how to challenge unnecessary complexity and focus teams on business value.\n- Can combine start-up-style experimentation with bank-grade governance, security, and operational resilience.\n\n## Equal Opportunity Employer\n\nThe MUFG Group is committed to providing equal employment opportunities to all applicants and employees and does not discriminate on the basis of race, color, national origin, physical appearance, religion, gender expression, gender identity, sex, age, ancestry, marital status, disability, medical condition, sexual orientation, genetic information, or any other protected status of an individual or that individual's associates or relatives, or any other classification protected by the applicable laws. Mitsubishi UFJ Financial Group (MUFG) is an equal opportunity employer. We view our employees as our key assets as they are fundamental to our long-term growth and success. MUFG is committed to hiring based on merit and organsational fit, regardless of race, religion or gender.\n\nAt MUFG, our colleagues are our greatest assets. Our Culture Principles provide a roadmap for how each of our colleagues must think and act to become more client-obsessed, inclusive and innovative. They reflect who we are, who we want to be and what we expect from one another. We are excited to see you take the next step in exploring a career with us and encourage you to spend more time reviewing them!\n\n## Our Culture Principles\n\n- Client Centric\n- People Focused\n- Listen Up. Speak Up.\n- Innovate & Simplify\n- Own & Execute",{"sql":120,"ai\u002Fml":120,"python":120,"etl\u002Felt":120,"data lakes":120,"guardrails":120,"data lineage":120,"generative ai":120,"observability":120,"llm evaluation":120,"api development":120,"data engineering":120,"data warehousing":120,"knowledge graphs":120,"machine learning":120,"data virtualization":120,"metadata management":120,"software engineering":120,"agentic architectures":120,"model context protocol (mcp)":120,"retrieval-augmented generation (rag)":120},4,[39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59],{"leadership":120,"communication":120,"stakeholder management":120,"talent development":120,"innovation":120,"collaboration":120,"strategic thinking":120,"problem solving":120,"decision making":120,"mentoring":120,"influencing":120},[60,61,62,63,64,65,66,67,68,69,70],{"code review":120,"ai governance":120,"responsible ai":120,"data governance":120,"data management":120,"risk management":120,"model governance":120,"technical hiring":120,"change management":120,"program management":120,"architecture review":120,"portfolio management":120,"production operations":120,"ai product development":120,"digital transformation":120,"engineering leadership":120,"enterprise ai strategy":120,"data platform architecture":120},[71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88],{"detected_language_jobad":127,"required":128},"en",[129],[130],{"language":131,"level":132},"ENGLISH","C1",15,[135,136],"Information and Communications Technology","Finance",[135,136],"https:\u002F\u002Fmufgub.wd3.myworkdayjobs.com\u002Fen-us\u002Fmufg-careers\u002Fjob\u002Fmufg-global-service-private-ltd---bengaluru-bcit\u002Fdata-ai-director_10079291-wd",1]