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