We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Sr Lead Software Engineer at JPMorgan Chase within the Asset & Wealth Management, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
At JPMorgan Chase, we are reimagining software engineering itself – by building an AI-Native SDLC Agent Fabric, a next generated ecosystem of autonomous, collaborative agents that transform every phase of the software delivery lifecycle. We are forming a foundational engineering team to architect, design, and build this intelligent SDLC framework levering multi-agent systems, AI toolchains, LLM Orchestration (A2A, MCP) and innovative automation solutions. If you’re passionate about shaping the future of engineering—not just building better tools, but developing a dynamic, self-optimizing ecosystem—this is the place for you.
Job responsibilities
- Works closely with software engineers, product managers, and other stakeholders to define requirements and deliver robust solutions.
- 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
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Experience in Software engineering using AI Technologies
- 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 CI/CD, Terraform, Kubernetes, Docker and APIs
- Familiarity with observability and monitoring platforms
- Strong analytical and problem-solving mindset.
- Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching senior engineers/leads on compliant usage patterns and controls.
Preferred qualifications, capabilities, and skills
- Experience with Azure or Google Cloud Platform (GCP).
- Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.