AI Foundational Model Engineer

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.

Vice President – Technical AI Foundation Model Engineer Role Summary The VP, Technical AI Foundation Model Engineer is responsible for designing, building, deploying, and optimizing enterprise-grade AI solutions powered by foundation models, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI architectures. The role translates AI concepts into secure, scalable, observable, and production-ready systems suitable for a highly regulated financial services environment. This individual serves as the technical lead across AI engineering initiatives, partnering closely with Product Management, Enterprise Architecture, Data Engineering, Platform Engineering, Cybersecurity, Risk, and Business stakeholders to deliver transformational AI capabilities.

Key Responsibilities Foundation Model Engineering • Design, build, and optimize enterprise AI solutions leveraging foundation models, LLMs, and agentic AI architectures. • Develop and maintain Retrieval-Augmented Generation (RAG) pipelines and semantic search capabilities. • Evaluate, benchmark, and recommend foundation models based on performance, cost, security, explainability, and business requirements. • Implement model orchestration, prompt engineering, agent frameworks, and AI workflow automation. [MUFG_AI_Ge...ck_Revised | PDF]

AI Platform & Architecture • Lead solution architecture for AI applications across cloud and enterprise platforms. • Design scalable model-serving architectures and AI APIs. • Optimize latency, throughput, resiliency, and cost efficiency of AI workloads. • Establish reusable frameworks and engineering patterns for enterprise AI delivery.

LLMOps / MLOps Leadership • Own end-to-end model lifecycle management, including: • Experimentation • Evaluation • Deployment • Monitoring • Rollback • Continuous improvement

• Implement observability, telemetry, and performance monitoring across AI solutions. • Define engineering standards and best practices for AI development and operations

Responsible AI & Governance • Ensure AI systems comply with enterprise requirements for: • Security • Privacy • Risk Management • Compliance • Auditability

• Implement controls for: • Hallucination mitigation • Prompt security • Model safety • Data protection • Human oversight

• Partner with Model Risk Management, Legal, and Compliance teams to operationalize Responsible AI principles.

Technical Leadership • Lead technical design reviews and architecture decisions. • Mentor engineers and establish engineering excellence practices. • Guide build-vs-buy evaluations for AI platforms and vendor solutions. • Drive innovation through experimentation with emerging AI technologies.

Business & Stakeholder Engagement • Translate business requirements into scalable AI architectures. • Collaborate with Product Managers and business teams to define solution requirements. • Present technical recommendations and tradeoffs to senior leadership. • Support executive decision-making regarding AI platform investments.

Required Qualifications Experience • 8-12+ years of experience in: • AI/ML Engineering • Software Engineering • Platform Engineering • Applied Machine Learning

• Demonstrated experience delivering production-grade AI solutions. • Previous experience building enterprise-scale AI platforms or AI-enabled products. • Experience working in regulated industries such as banking, financial services, insurance, healthcare, or government preferred •

Technical Expertise Strong hands-on expertise in: AI & Machine Learning • Foundation Models • Large Language Models (LLMs) • Agentic AI Systems • Retrieval-Augmented Generation (RAG) • Embeddings • Semantic Search • Vector Databases • Prompt Engineering • Fine-Tuning Techniques

Engineering Stack • Python • PyTorch • TensorFlow • Hugging Face • LangChain • LlamaIndex • Semantic Kernel

Cloud & Platform Engineering • AWS and/or Azure • Kubernetes • Containerized AI Workloads • CI/CD Pipelines • Model Serving Platforms • API Architecture

AI Operations • MLOps • LLMOps • Model Monitoring • Evaluation Frameworks • Performance Optimization • Cost Optimization •

Leadership Competencies • Strong architecture and systems-thinking mindset. • Ability to influence across engineering, product, architecture, and risk organizations. • Executive communication skills. • Strong problem-solving and decision-making capability. • Ability to balance innovation with governance requirements. • Experience leading technical teams and mentoring engineers.

Success Metrics The VP, Technical AI Foundation Model Engineer will be measured on: Platform & Engineering Outcomes • Production AI deployments • Platform reliability and scalability • Performance optimization • Engineering productivity

AI Quality Metrics • Model accuracy • Retrieval effectiveness • Hallucination reduction • User adoption and satisfaction

Operational Metrics • AI platform utilization • Cost efficiency • Time-to-production • Technical debt reduction

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