AI Foundational Model Engineer

Mitsubishi UFJ Financial Group•Jersey City, NJ

2d•Hybrid

About The Position

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.

Requirements

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
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
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.
Bachelor's degree in Computer Science or a closely-related discipline, or an equivalent combination of formal education and experience

Nice To Haves

Experience working in regulated industries such as banking, financial services, insurance, healthcare, or government

Responsibilities

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.
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.
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.
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.
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.
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.