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, 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 modernization 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., Lang Graph, MCP)
• Proficiency in automation and continuous delivery methods (CI/CD, DevOps). 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 practices

Preferred qualifications, capabilities, and skills

• Experience with post-trade financial platforms (e.g., Athena, Quartz, Sec DB)
• 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