Join us to shape the next generation of AI solutions and make a lasting impact on the industry. You will lead a talented team, collaborate across business lines, and drive innovation through advanced AI platforms. Your expertise will help us stay ahead in a rapidly evolving field, offering you opportunities for growth and leadership. At JPMorganChase, we value your vision and empower you to push the boundaries of what’s possible.
Job Summary:
As an AI Engineering Director within LLM Suite engineering, AI/ML & Data Platforms team at JPMorganChase, you will lead a group of experts to develop horizontal capabilities through APIs, libraries, and thought leadership. You will collaborate with Line of Business AI teams to address priority use cases, design and build services, and promote best practices in AI. Your role involves mentoring AI engineers and ensuring we remain at the forefront of AI advancements. You will help shape our team culture and drive impactful solutions across the firm.
Job Responsibilities:
1. Lead the architecture and implementation of scalable, reliable LLM-based systems and agentic AI platforms for enterprise use cases.
2. Design and build production-grade AI systems, including agents, harnesses, skills, memory architectures, guardrails, and tool-use workflows.
3. Architect and implement retrieval and context-engineering patterns such as embeddings, semantic search, grounding, summarization, and prompt/version management.
4. Engineer cloud-native AI platforms on AWS using ECS, EKS, Lambda, SQS, SNS, containerized workloads, and DynamoDB-backed distributed architectures.
5. Optimize AI systems for latency, throughput, scalability, caching, context efficiency, and cost.
6. Build APIs, integrations, MCP Servers, and reusable platform capabilities to connect AI systems with enterprise platforms, tools, and workflows.
7. Establish evaluation, experimentation, regression, and observability frameworks to continuously improve AI system quality, reliability, and agent behavior.
8. Mentor senior engineers and influence engineering direction through code reviews, architecture discussions, technical standards, and cross-organizational leadership.
Required Qualifications, Capabilities, and Skills:
9. PhD or deep experience using LLMs and Agents to develop scalable applications, or experience in a top commercial AI research lab.
10. Strong understanding of AI fundamentals and practical experience with data analysis and experimental design.
11. Recent hands-on experience training and deploying models and pipelines.
12. Familiarity with distributed computing patterns for training, serving, and persistence of state.
13. Experience building and leading high-performing AI teams.
14. Exceptional verbal and written communication skills, with the ability to convey complex technical concepts to diverse audiences.
15. Ability to influence key decision makers with compelling technical arguments.
Preferred Qualifications, Capabilities, and Skills:
16. Experience with enterprise-scale AI platform development.
17. Knowledge of industry-standard AI evaluation and observability frameworks.
18. Expertise in cloud-native architectures and container orchestration.
19. Proven track record of cross-functional collaboration and leadership.
20. Familiarity with MCP protocols and enterprise integration patterns.
21. Advanced skills in optimizing AI systems for performance and cost.
22. Demonstrated commitment to fostering an inclusive and innovative team culture.