Lead Software Engineer - Python and AI

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible. As a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank, Post-Trade Technology team, 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 Designs and builds production-grade agentic AI systems including LLM orchestration layers, RAG pipelines, vector databases, and MCP-based tool integrations Develops and reviews secure, high-quality code and debugs solutions written by team members or generated by AI models Architects multi-agent and single-agent setups, authors skill files, and writes technical RFCs for new AI capabilities Contributes to cloud-native deployments (AWS ECS), CI/CD pipelines, and infrastructure modernisation alongside platform engineering squads Builds and iterates on agentic patterns — multi-hop agents, vector search, automated code generation — from POC through UAT to full production rollout Translates regulatory operations and business requirements into precise technical designs and delivers against quarterly commitments Identifies opportunities to automate recurring operational issues, reducing manual toil and improving platform stability Participates actively in sprint ceremonies, code reviews, and architecture discussions as a senior technical contributor Contributes to an internal developer productivity accelerator as a technical mentor and active builder across 10+ cross-functional teams 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. Required qualifications, capabilities, and skills Formal training or certification on software engineering concepts and 5+ years of applied experience Hands-on practical experience delivering system design, application development, testing, and operational stability Advanced proficiency in Python; strong object-oriented programming fundamentals Proven experience building and deploying LLM-based or agentic AI systems in production Deep understanding of RAG architecture, vector databases, and AI agent frameworks (e.g., LangGraph, MCP) Proficiency in automation and continuous delivery methods (CI/CD, DevSecOps) Proficient across all phases of the Software Development Life Cycle. Advanced understanding of agile methodologies and application resiliency patterns Practical cloud-native experience on AWS (ECS, Lambda, S3 or equivalent) Strong analytical and problem-solving skills with ability to break down complex technical problems independently 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 practice Preferred Qualifications, Capabilities, and Skills Experience with post-trade financial platforms (e.g., Athena, Quartz, SecDB) In-depth knowledge of financial services industry IT systems, trade reconciliation and regulatory reporting workflows Experience with AI / ML / Vibe coding and agentic development workflows Knowledge of distributed computing, data modeling, and performance engineering Familiarity with data lineage, data contracts, and data governance frameworks Experience with ServiceNow, Jira, or Confluence API integrations Regression testing and observability tooling experience in large-scale platforms