Lead Software Engineer - Python and AI
Explicitly mentions vibe coding and agentic development workflows; role centers on building and operating agentic AI systems.
About the Role
Lead Software Engineer role focused on designing, building, and delivering production-grade Python and AI systems (LLM orchestration, RAG, vector DBs, agent frameworks) within JPMorgan Chase's Commercial & Investment Bank. The position leads technical design and delivery, drives adoption of AI-assisted engineering practices, and contributes to cloud-native deployments and CI/CD for secure, scalable production systems.
Job Description
Role
Lead Software Engineer (Python & AI) responsible for designing and delivering production-grade agentic AI systems and platform capabilities within the Commercial & Investment Bank. Acts as a core technical contributor and technical mentor across cross-functional teams, translating business and regulatory requirements into robust technical designs and production rollouts.
Key Responsibilities
- Design and build production agentic AI systems including LLM orchestration layers, RAG pipelines, vector databases, and MCP-based tool integrations.
- Architect multi-agent and single-agent setups, author skill files, and write technical RFCs for new AI capabilities.
- Develop, review, and debug secure, high-quality code (including code generated by AI models).
- Contribute to cloud-native deployments and infrastructure modernization (AWS ECS), CI/CD pipelines, and platform engineering collaboration.
- Build and iterate on agentic patterns (multi-hop agents, vector search, automated code generation) from POC through UAT to production.
- Identify automation opportunities to reduce operational toil and improve platform stability; participate in sprint ceremonies, code reviews, and architecture discussions.
- Drive team adoption of enterprise-authorized AI-assisted engineering practices and establish validation standards (secure coding, peer review, automated testing).
- Mentor and accelerate developer productivity across 10+ cross-functional teams.
Requirements
- Formal training or certification in software engineering concepts and 5+ years of applied experience.
- Advanced proficiency in Python and 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) and practical cloud-native experience on AWS (ECS, Lambda, S3 or equivalent).
- Advanced understanding of agile methodologies, application resiliency patterns, and operational stability.
- Strong analytical and problem-solving skills; demonstrated experience leading use of AI-assisted development tools and setting expectations for validating AI outputs.
- Strong understanding of responsible AI practices, data sensitivity, secure handling of inputs/outputs, and coaching teams on compliant adoption.
Preferred Qualifications
- Experience with post-trade financial platforms (Athena, Quartz, Sec DB) and financial services IT systems, trade reconciliation, and regulatory reporting.
- Experience with AI/ML, vibe coding, and agentic development workflows.
- Familiarity with distributed computing, data modeling, data lineage, data contracts, data governance, ServiceNow/Jira/Confluence API integrations, regression testing, and observability tooling.