Software Engineer III-Gen AI/RAG/Python

JP Morgan Chase Bank; Jersey City (NJ); On-site; USD 130,000 - 190,000; Full time

Job summary

JP Morgan Chase in Jersey City seeks a Software Engineer III-Gen AI/RAG/Python to join a fast-moving AI pod within Consumer & Community Banking – Wealth Management. You will work hands-on with LLMs, SLMs, agentic frameworks and AWS, building AI solutions used by business users and deployed quickly.

The role emphasizes secure coding, production-grade delivery, and collaboration with business users to iterate rapidly. Strong Python and Gen AI experience is required.

Qualifications

• Formal training or certification on software engineering concepts and 3+ years of applied experience.
• Strong Python skills and hands-on experience with Gen AI and AI/ML frameworks.

Responsibilities

• Build and ship generative AI and agentic AI applications end to end, from prototype to production.
• Develop agentic workflows, including tool-using agents and multi-agent collaboration.
• Contribute to autonomous AI capabilities with appropriate guardrails and controls.
• Implement orchestration and retrieval-augmented generation (RAG) pipelines, integrating AI solutions with internal systems.
• Build and run model and agent evaluations to maintain high-quality outcomes.

Skills

Python

Gen AI

AI/ML frameworks

AWS

CI/CD

Secure coding

Education

Formal training/certification in software engineering

Tools

LangGraph

Google ADK

AI tooling

Job description

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.

As a Software Engineer III-Gen AI/RAG/Python at JPMorgan Chase within Consumer & Community Banking – Wealth Management, you'll join a small, fast-moving AI pod that builds and deploys generative and agentic AI solutions directly with business users. You'll work hands-on with LLMs, SLMs, agentic frameworks and AWS, get exposure to autonomous AI and agent-to-agent systems, and see your work go live quickly.

Job responsibilities

• Build and ship generative AI and agentic AI applications end to end, from prototype to production.
• Develop agentic workflows, including tool-using agents and multi-agent collaboration.
• Contribute to autonomous AI capabilities with appropriate guardrails and controls.
• Implement orchestration and retrieval-augmented generation (RAG) pipelines, integrating AI solutions with internal systems.
• Build and run model and agent evaluations to maintain high-quality outcomes.
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Process and analyze large datasets that support AI solutions.
• Develop components for concurrent, multi-user, and real-time applications.
• Write secure, high-quality production code and deploy on AWS using CI/CD.
• Partner with business users to gather feedback and iterate quickly, while contributing to engineering communities of practice and fostering a culture of diversity, opportunity, inclusion, and respect.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 3+ years of applied experience.
• Strong Python skills and hands-on experience with Gen AI and AI/ML frameworks, paired with a bias for action and comfort working to tight timelines.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
• Hands-on experience with agent frameworks such as LangGraph, Google ADK, or similar.
• Practical experience building LLM-based applications using prompt engineering, skill and tool development, and RAG.
• Experience evaluating and benchmarking models or LLM-based applications.
• Experience working with large datasets.
• Understanding of concurrent, multi-user, and real-time application design.
• Experience with AWS or another major cloud platform.
• Strong understanding of the SDLC and agile practices, including CI/CD, application resiliency, and security.

Preferred qualifications, capabilities, and skills

• Exposure to SLMs, LRMs and model fine-tuning.
• Hands-on AI projects, hackathons or personal builds.
• Familiarity with vector databases and streaming architectures.
• Front-end experience for AI user experiences.