Applied AI ML Lead - LLM Suite Engineering

Build what’s next in applied AI at JPMorgan

Chase - where your work shapes how teams use intelligent systems ’ll lead hands‑on engineering for agentic and GenAI capabilities that power the LLM Suite platform. This role offers a mix of deep technical problem‑solving, architecture ownership, and collaboration with talented builders. If you enjoy turning ambiguity into reliable production systems, you’ll thrive here. Join a team that values craft, security, and learning.

As an Applied AI ML Lead in LLM Suite Engineering, you will design and deliver production‑grade AI/ML and agentic solutions that integrate seamlessly with existing systems. You will own technical direction across architecture, implementation, and operational stability, with a strong focus on secure, high‑quality software. You will partner with peers across engineering to identify patterns and improve standards, reliability, and scalability.

You will help evolve the platform using modern public cloud services and agentic frameworks. You will contribute to a collaborative culture through communities of practice and emerging‑technology events. You will explore and operationalize emerging patterns such as agent‑to‑agent communication, model context protocols, and agentic orchestration, turning early‑stage concepts into scalable, production‑ready capabilities.

• Design, develop, and troubleshoot software solutions using creative approaches to solve complex technical challenges
• Write secure, high‑quality production code and maintain algorithms that integrate with existing systems
• Create architecture and design artifacts for complex applications, ensuring design constraints are met through delivery
• Build AI/ML solutions and agentic systems for the LLM Suite platform using public cloud architecture (Azure, AWS) and modern agentic frameworks
• Implement GenAI services leveraging Azure OpenAI models and AWS Bedrock
• Identify hidden problems and patterns in data proactively to improve coding standards and system architecture
• Participate in software engineering communities of practice and events focused on emerging technologies

• Computer science degree or equivalent practical experience
• Hands‑on experience with system design, application development, testing, and operational stability
• Proficiency in Python (FastAPI)
• Experience building microservices and APIs
• Experience with elastic compute, No
  
  SQL databases, and messaging queues
• Strong understanding of the Software Development Life Cycle
• Solid grasp of CI/CD, application resiliency, and security

• Experience implementing GenAI services leveraging Azure OpenAI models and AWS Bedrock
• Proficiency working with large language models and building agents with Lang Graph
• Experience developing, debugging, and maintaining code in a large corporate environment using modern programming and database querying languages
• Experience with containerization
• Knowledge of agent‑to‑agent (A2A) communication concepts
• Familiarity with Model Context Protocol (MCP)
• Experience with agentic orchestrators, personal AI assistants, or AI skills development