Lead Software Engineer - Java and AI/ML

Lead Software Engineer - Java and AI/ML

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- Global Banking, 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

• Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems

• Develops secure high-quality production code (primarily Java / Spring Boot), and reviews and debugs code written by others

• Leads the design and delivery of scalable microservices/APIs within the Spring ecosystem (e.g., Spring MVC/WebFlux, Spring Data, Spring Security), with strong testing practices

• Proactively improves engineering productivity using AI4Tech practices, including hands-on use of Copilot/Claude Code for refactoring, test generation, documentation, and code modernization—while ensuring correctness and secure coding standards

• Builds and operationalizes AI agents that integrate with engineering workflows (e.g., PR review assistants, runbook/support agents, remediation assistants), including tool/function calling, structured outputs, and safety/guardrails

• Uses Python as needed for AI/agent prototyping, automation, evaluation harnesses, or glue code/integrations

• Adds to team culture of diversity, opportunity, inclusion, and respect

• 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

• Hands-on practical experience delivering system design, application development, testing, and operational stability

• Advanced Java development experience with strong fundamentals (OO design, concurrency, performance, debugging)

• Strong hands-on experience with Spring Boot and related frameworks (REST APIs, security, persistence), Elastic search, plus unit/integration testing. 

• AI4Tech hands-on experience using Copilot/Claude Code (or similar approved tools) to accelerate delivery while maintaining code quality, security, and test coverage

• Experience building AI agents / LLM-enabled workflows, including prompt discipline, grounding/verification strategies, and safe handling of sensitive data

• Some experience or working knowledge of Python (scripting, automation, or AI/agent prototyping)

• Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security

• Practical experience on Kubernetes

• 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 

• Experience designing and operating production-grade agentic systems (observability, evals, prompt/versioning, fallbacks, rate limits, and guardrails)

• Experience with modern microservice patterns (resiliency, distributed tracing, event-driven design)

Experience Level

Senior Level