Lead Software Engineer - AI ML

As a Lead Software Engineer at JPMorgan Chase within the Asset & Wealth Management, you have the opportunity to impact your career and provide an adventure where you can push the limits of what's possible. 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. - **Job Responsibilities:** - Works closely with software engineers, product managers, and other stakeholders to define requirements and deliver robust solutions. - Designs and Implements 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. - **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. - **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.,