Vice President - Technical AI Foundation Model EngineerRole SummaryThe 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 ResponsibilitiesFoundation 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 QualificationsExperience- 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 ExpertiseStrong 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 MetricsThe 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
Governance Metrics- Compliance adherence
- Security posture
- Responsible AI control effectiveness
- Audit readiness
Education:- Bachelor's degree in Computer Science or a closely-related discipline, or an equivalent combination of formal education and experience
"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: $149-205K
- Non-New York / New Jersey: $149-188K
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.
