Build what’s next in applied AI at JPMorganChase - where your work shapes how teams use intelligent systems at scale. You’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.
Job Responsibilities
Design, develop, and troubleshoot software solutions using creative approaches to solve complex technical challengesWrite secure, high-quality production code and maintain algorithms that integrate with existing systemsCreate architecture and design artifacts for complex applications, ensuring design constraints are met through deliveryBuild AI/ML solutions and agentic systems for the LLM Suite platform using public cloud architecture (Azure, AWS) and modern agentic frameworksImplement GenAI services leveraging Azure OpenAI models and AWS BedrockIdentify hidden problems and patterns in data proactively to improve coding standards and system architectureParticipate in software engineering communities of practice and events focused on emerging technologiesRequired Qualifications, Capabilities, and Skills
Computer science degree or equivalent practical experienceHands-on experience with system design, application development, testing, and operational stabilityProficiency in Python (FastAPI)Experience building microservices and APIsExperience with elastic compute, NoSQL databases, and messaging queuesStrong understanding of the Software Development Life CycleSolid grasp of CI/CD, application resiliency, and securityPreferred Qualifications, Capabilities, and Skills
Experience implementing GenAI services leveraging Azure OpenAI models and AWS BedrockProficiency working with large language models and building agents with LangGraphExperience developing, debugging, and maintaining code in a large corporate environment using modern programming and database querying languagesExperience with containerizationKnowledge of agent-to-agent (A2A) communication conceptsFamiliarity with Model Context Protocol (MCP)Experience with agentic orchestrators, personal AI assistants, or AI skills development