Lead Software Engineer

JPMorganChase is building the next generation of intelligent, cloud-native systems and seeks a Lead Software Engineer to help lead the way. In this role, you will own the delivery of complex, AI-powered software initiatives, design and build intelligent systems, and ensure reliable operations across a multi-cloud environment.

Responsibilities:

• Lead initiatives end-to-end — from requirements clarification and architecture through implementation, testing, release, and production support — with strong ownership and minimal supervision
• Design and implement AI solutions using large language models and modern agent patterns, including prompting strategies, tool/function calling, retrieval patterns, routing, and memory/state management where applicable
• Build guardrails, evaluation frameworks, monitoring pipelines, and cost/latency optimizations for production LLM-based systems
• Design, build, and operate REST and gRPC APIs and microservices, defining clear contracts using OpenAPI and Protobuf while ensuring backward compatibility, authentication, rate limiting, and observability
• Apply resilience engineering patterns — including timeouts, retries, and circuit breakers — to ensure reliable, production-grade service behavior
• Build and maintain well-tested, maintainable Python services and automation with clear packaging, dependency management, and architectural standards
• Own data design and implementation, including schema design, data access patterns, and complex SQL optimization aligned to performance and reliability requirements
• Build and manage infrastructure as code using Terraform, supporting containerized deployments via Kubernetes and CI/CD pipelines across multi-cloud environments
• Drive engineering excellence across code quality, testing strategy, performance, reliability, and operational rigor, including leading root-cause analysis for complex production issues
• Mentor engineers, provide technical guidance, and establish standards for delivery and engineering practices across the team
• 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 automations