JPMorgan Chase—Data Scientist Lead - Agentic AI Solutions

Job description

Description: As the next push into this investment, JPMC is hiring the best talents to join our AI engineering team. We are executing like a startup and building the next generation technology that combines JPMC unique data and full-service advantage to develop high impact AI applications and platforms in the financial services industry. We are looking for people who are excited about the opportunity. As a Data Scientist Lead at JPMorgan Chase, you design and deliver trusted market-leading technology products in a secure, stable, and scalable way. Job Responsibilities - Designs and Implement LLM-driven agent services for design, code generation, documentation, test creation and observability on AWS - Develops orchestration and communication layers between agents using frameworks like A2A SDK, LangGraph, or Auto Gen - Integrates AI agents with toolchains such as Jira, Bitbucket, Github, Terraform and monitoring platforms - Collaborates on system design, SDK development and data pipelines supporting agent intelligence - Provides technical leadership, mentorship, and guidance to junior engineers and team members. - Drives adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test acceleration, release readiness, incident/root-cause analysis), while establishing measurable validation standards (secure coding, peer review, automated testing) and promoting reuse of proven patterns and automation within the SDLC/TLM toolchain. - Applies knowledge of tools within the Software Development Life Cycle toolchain, including approved AI-assisted development and automation capabilities, to improve the value realized by automation at scale. Required qualifications, capabilities and skills - BE/B. Tech, ME/MS or PhD degree in Computer Science, or Machine learning related field. - Strong hands-on skills in Python, Pydantic, FastAPI, LangGraph, and Vector Databases for building RAG based AI agent solutions integrating with multi-agent orchestration frameworks and deploying end-to-end pipelines on AWS (EKS, Lambda, S3, Terraform) - Experience with LLMs integration, prompt/context engineering, AI Agent frameworks like Langchain/LangGraph, Autogen, MCPs, A2A. - Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics. - Expert in at least one of the following areas: Natural Language Processing, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis. - Solid understanding of Cloud (AWS/ Azure/ GCP) and DevOps (CI/CD, Terraform, Kubernetes, Docker and APIs