Director, Distinguished Engineer, Enterprise AI PlatformsPosition SummaryMUFG 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 PurposeThe 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 Responsibilities1. 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/unstructured data integration.
- Shape how AI Ready Data, data products, metadata, and enterprise knowledge are exposed safely and consistently to AI applications and agents.
5. Strategic Technical Direction- Create and maintain architecture roadmaps for enterprise AI capabilities, including agentic AI, multi-agent orchestration, enterprise knowledge graphs, evaluation at scale, self-service developer tooling, and platform interoperability.
- Advise senior technology and business leaders on build-versus-buy decisions, balancing speed, cost, control, reuse, vendor risk, and long-term enterprise economics.
- Evaluate emerging AI technologies, including LLM providers, open-weight models, AI frameworks, agent orchestration tools, evaluation platforms, observability products, vector databases, and AI security solutions.
- Establish technical principles, reference architectures, design standards, and reusable patterns that reduce fragmented experimentation and promote governed enterprise adoption.
6. Cross-functional Influence and Enablement- Partner with business AI leads, product owners, engineers, data teams, architects, risk partners, and platform teams to convert business demand into scalable AI capabilities.
- Mentor senior engineers, architects, and solution teams on AI platform design, GenAI engineering patterns, and enterprise-grade production practices.
- Communicate complex AI architecture concepts clearly to executives, business sponsors, risk partners, and technical teams.
- Act as a senior technical voice in architecture forums, AI governance forums, platform prioritization discussions, and strategic vendor evaluations.
Required Experience- 15+ years of experience in enterprise software engineering, platform engineering, architecture, data platforms, distributed systems, or related technology leadership roles.
- Hands-on experience architecting and delivering production-grade AI, GenAI, LLM, data, or enterprise platform capabilities.
- Experience designing enterprise AI platform services such as LLM gateways, model routing, provider abstraction, RAG services, agentic workflows, AI observability, model evaluation, prompt/context management, and AI guardrails.
- Strong background in cloud-native architecture, APIs, microservices, event-driven systems, containerization, CI/CD, infrastructure automation, and production operations.
- Deep understanding of enterprise data architecture, data governance, metadata, lineage, structured/unstructured data integration, data quality, and access-control patterns.
- Experience with security, resilience, monitoring, auditability, cost optimization, and operational controls in regulated environments.
- Proven ability to influence architecture and engineering direction across multiple teams, with strong executive communication skills.
Preferred Technical Skills- AI platform and LLMOps experience, including model gateways, provider abstraction, prompt management, observability, evaluation, guardrails, agent runtime, and AI cost management.
- Familiarity with GenAI frameworks and tools such as LangChain, LangGraph, CrewAI, LiteLLM, Ragas, Hugging Face, PyTorch, TensorFlow, SageMaker, or comparable platforms.
- Experience with model providers such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google/Gemini, open-weight models, and enterprise integration patterns.
- Experience with data and knowledge platforms such as Snowflake, Databricks, Spark, Kafka, Airflow, vector search, OpenSearch/Elasticsearch, knowledge graphs, metadata platforms, and document intelligence.
- Strong engineering experience with Java, Python, Scala, JavaScript/TypeScript, SQL, Spring, FastAPI, Node.js, GraphQL, REST/OpenAPI, AWS, Kubernetes, Docker, Terraform, GitHub, and CI/CD.
- Understanding of governance and control patterns, including access controls, entitlements, data privacy, audit logging, model monitoring, AI inventory, model risk, secure-by-design reviews, and policy-as-code.
Education and Certifications- Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related field required.
"Visa sponsorship/support is based on business needs. We do not anticipate providing visa sponsorship/support for this position."The typical base pay range for this role is as follows:
- New York / New Jersey: $ 250-350K
- Non-New York / New Jersey: $ 250-300K
depending on job-related knowledge, skills, experience and location. This role may also be eligible for certain discretionary performance-based bonus and/or incentive compensation. Additionally, our Total Rewards program provides colleagues with a competitive benefits package (in accordance with the eligibility requirements and respective terms of each) that includes comprehensive health and wellness benefits, retirement plans, educational assistance and training programs, income replacement for qualified employees with disabilities, paid maternity and parental bonding leave, and paid vacation, sick days, and holidays. For more information on our Total Rewards package, please click the link below.
Our hybrid work schedule is four days on-site and work remotely one day per week.
