Lead Software Engineer - Python and AI/ML

Job Description We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a AIML Lead 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
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• 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.
• Provides technical leadership, mentorship, and guidance to junior engineers and team members.

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
• Demonstrated experience leading effective use of approved AI-assisted software development tools (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 engineers on safe, compliant adoption within delivery practices
• Familiarity with observability and monitoring platforms
• Strong analytical and problem-solving mindset.

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.

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