Director, Distinguished Engineer, Enterprise AI Platforms

About this role

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.

The selected colleague will work at an MUFG office or client sites four days per week and work remotely one day. A member of our recruitment team will provide more details.

Director, Distinguished Engineer, Enterprise AI Platforms

Position Summary MUFG Americas is building the foundational AI platforms, data and knowledge capabilities, governance patterns, and reusable engineering services needed to scale AI safely across the enterprise. The Director, Principal AI Platform Architect / Distinguished AI Engineer will be a senior hands-on technical leader responsible for architecting and evolving enterprise AI platforms that enable business-specific AI solutions to be built in a federated manner on common, governed, reusable foundations. This role will lead core AI platform capabilities including LLM gateways, model-provider abstraction, agent runtime, orchestration, RAG and knowledge integration, AI observability, evaluation frameworks, security guardrails, FinOps, and reusable engineering patterns. The successful candidate will combine deep engineering expertise, architectural judgment, product thinking, and strategic leadership to help MUFG move from early AI experimentation to governed, enterprise-grade AI adoption. This Director-level technical leadership role requires the ability to set technical direction, influence senior stakeholders, mentor engineering teams, advise on build-versus-buy decisions, and translate emerging AI capabilities into reliable, secure, compliant, and reusable enterprise platforms.

Role Purpose The purpose of this role is to build the enterprise AI foundation that allows MUFG to centralize hard-to-build, reusable, and control-intensive capabilities while enabling business and technology teams to innovate faster at the edge. The role will help ensure AI solutions are not built as isolated point solutions, but instead leverage shared services, trusted data, common governance controls, reusable components, and scalable engineering patterns. It supports MUFG’s layered AI strategy across common AI services, trusted data and knowledge, and AI Hub / marketplace capabilities for collaboration and reuse.

Key Responsibilities 1. Enterprise AI Platform Architecture • Lead the architecture and evolution of MUFG’s enterprise AI platform capabilities, including LLM gateways, model routing, provider abstraction, agent runtime, orchestration, prompt/context management, observability, and FinOps. • Define reusable architecture patterns for AI applications, RAG pipelines, agentic workflows, AI-assisted automation, model connectivity, and embedded AI services. • Design platform services that support multiple model providers, cloud patterns, data sources, business domains, risk tiers, and AI consumption models, including tech-built, citizen-built, vendor-enabled, and embedded AI solutions. • Partner with Enterprise Architecture, Security, Infrastructure, Data Architecture, AI Governance, and application teams to ensure platforms are secure, scalable, resilient, auditable, and aligned with enterprise standards.

2. Engineering Leadership and Hands-on Delivery • Serve as a hands-on technical leader who reviews architecture, guides engineering decisions, develops prototypes, and helps teams solve complex design and implementation challenges. • Lead development of reusable AI platform components, including model gateways, agent frameworks, tool registries, prompt libraries, evaluation pipelines, data connectors, orchestration patterns, and SDKs/APIs. • Establish production-grade engineering patterns for resilience, observability, latency, rate limiting, failover, caching, tenant fairness, usage attribution, cost optimization, CI/CD, automated testing, and production readiness. • Create implementation blueprints that enable engineering teams and approved business builders to “compose, not rebuild.”

3. AI Governance by Design • Embed governance, risk, security, privacy, monitoring, auditability, and human oversight into AI platform architecture from the start. • Partner with AI Governance, Operational Risk, Model Risk, Compliance, Legal, Privacy, Cybersecurity, and Data Governance teams to translate policy expectations into practical platform controls. • Define technical control patterns for access control, data classification, entitlement-aware retrieval, model usage monitoring, prompt/output logging, content filtering, human-in-the-loop workflows, exception handling, and audit trails. • Support AI use-case intake and routing by assessing technical feasibility, reusability, architecture fit, risk implications, and platform readiness.

4. Data, Knowledge, and Context Engineering • Architect AI solutions that use trusted enterprise data, metadata, documents, ontologies, context graphs, and knowledge layers to improve relevance, explainability, and business usefulness. • Partner with Data Architecture and Data Governance teams to ensure AI solutions use high-quality, governed, lineage-aware, entitlement-controlled data. • Define patterns for RAG, hybrid search, semantic retrieval, vector stores, knowledge graphs, metadata enrichment, document intelligence, and structured

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