Sr Lead Software Engineer - Agentic AI Engineer

JPMorganChase is a financial services firm providing a broad range of financial products and services. The company is seeking a Sr Lead Software Engineer to design, build, deploy, and operate enterprise-grade AI solutions, including RAG pipelines, agentic AI systems, and LLM-powered workflows. The role also involves leading technical evaluations, improving operational stability, and influencing stakeholders across teams.

Responsibilities:

• Leverage AI-powered coding assistants (e.g., GitHub Copilot, Claude) as core tools in daily development workflows — writing, reviewing, debugging, and refactoring code with speed and precision
• Validate, critique, and iterate on AI-generated outputs rather than accepting them uncritically; apply sound engineering judgment to AI suggestions
• Continuously evaluate emerging AI tools and techniques, driving adoption where they deliver measurable productivity and quality gains
• Design, build, and deploy enterprise-grade AI solutions including Retrieval-Augmented Generation (RAG) pipelines, agentic AI systems, and LLM-powered workflows
• Architect AI systems with production-level concerns: scalability, cost management, latency, data privacy, hallucination mitigation, and observability
• Design, build, and deploy agentic solutions with enterprise grade identity, guardrails, tracing etc
• Execute creative software solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches. Develop secure, high-quality production code and review and debug code written by others
• Apply strong systems thinking — understand how components connect end-to-end, where failures occur, and how changes propagate across distributed systems
• Identify opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability
• Lead evaluation sessions with external vendors, startups, and internal teams to probe architectural designs, technical credentials, and applicability within existing systems
• Influence stakeholders and drive alignment across teams without direct authority, and own outcomes end-to-end — take accountability when things go well and when they don't