Data/AI Director

Job description

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. 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/ML, 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 Innova