Data Scientist Lead
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What you'll do
- 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.
What they're looking for
- 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
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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
Company
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