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
