Data Scientist Lead, Agentic Solutions

About the Role

As a Data Scientist Lead at JPMorgan Chase, you design and deliver trusted market-leading technology products in a secure, stable, and scalable way.

Key Responsibilities

• Designs and Implements 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.

Requirements & Tech Stack

• 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.
• Solid understanding of Cloud (AWS / Azure / GCP) and DevOps (CI/CD, Terraform, Kubernetes, Docker and APIs).

Qualifications

• BE/B. Tech, ME/MS or PhD degree in Computer Science, or Machine Learning related field.
• 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.