About The Position

Join our Data Analytics Reporting Team (DART) to build modular agentic components and ship production-ready AI features. You’ll leverage Python, AWS, and leading agentic frameworks to automate workflows, scale securely, and deliver measurable business outcomes. As a Data Scientist Associate Sr within the Data Analytics Reporting Team at JPMorganChase, you will design, build, and maintain modular components for agentic systems that automate and orchestrate workflows across business platforms. You will own workstreams and Jira stories, delivering high-quality, scalable Python code and features within larger AI/ML initiatives. You will containerize and deploy solutions to cloud environments using Docker and infrastructure as code with Terraform, and collaborate closely with cross-functional partners to integrate agentic capabilities into existing systems. You will stay current on agentic frameworks such as LangGraph, Google ADK, and AutoGen, as well as LLMs and GenAI techniques including retrieval-augmented generation, applying them pragmatically to solve business problems. You will document designs, communicate progress and technical concepts to stakeholders, and uphold code quality, security, and compliance through testing and peer reviews. Exposure to Kubernetes and EKS, MLOps tooling, and core AWS services (S3, RDS, CloudFormation) will help you drive resilient, production-grade solutions aligned to business goals

Requirements

  • Bachelor’s degree with 3+ years’ experience or Master’s degree with 1+ year experience in a quantitative field (e.g., Computer Science, Data Science, Applied Mathematics, Statistics, Engineering).
  • Hands-on experience developing and delivering agentic system components or autonomous AI features in production environments.
  • Strong Python programming skills, with a focus on modular, scalable, and maintainable code.
  • Experience with AWS cloud platform.
  • Familiarity with agentic frameworks (LangGraph, Google ADK, AutoGen) and integrating AI capabilities into business processes.
  • Ability to work collaboratively in agile, cross-functional teams and deliver on assigned workstreams.
  • Excellent problem-solving, communication, and documentation skills.
  • Exposure to agentic system design patterns, orchestration, and workflow automation.

Nice To Haves

  • Experience with Large Language Models (LLMs), GenAI, and retrieval-augmented generation (RAG).
  • Experience with distributed computing, cloud orchestration (Kubernetes, EKS), and MLOps tools.
  • Financial services industry experience.
  • Ability to design and evaluate autonomous system features aligned with business goals.
  • Experience with additional cloud services (EKS, S3, RDS, CloudFormation).
  • Experience with containerization (Docker), and infrastructure as code (Terraform).

Responsibilities

  • Build, test, and maintain modular components for agentic systems, enabling intelligent automation and orchestration within business platforms.
  • Take ownership of individual workstreams and Jira stories, delivering high-quality code and features as part of larger AI/ML initiatives.
  • Work closely with team members to integrate agentic capabilities into existing systems and support solution delivery.
  • Containerize and deploy agentic system components on cloud platforms, following established DevOps and infrastructure-as-code practices.
  • Stay current with advancements in agentic frameworks (e.g., LangGraph, Google ADK, AutoGen), LLMs, and GenAI, and apply these technologies to assigned tasks and features.
  • Document design decisions, implementation details, and results; communicate progress and technical concepts to team members and stakeholders.
  • Ensure code quality, security, and compliance through testing, peer reviews, and adherence to best practices
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