Java AI Lead Software Engineer

JPMorganChase•Columbus, OH

About The Position

Own end-to-end delivery of complex backend components, embedding AI-native capabilities (agentic workflows, LLM-powered features) into core product experiences. Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems. Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems. 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. Participate actively in design reviews and architecture discussions. Identify performance bottlenecks and propose optimizations. Mentor junior engineers through code reviews and technical guidance. Strong problem-solving and debugging skills. Ability to work independently on ambiguous problems. Clear technical communication with peers and stakeholders.

Requirements

Formal training or certification on software engineering concepts and 7+ years applied experience.
Experience in designing and developing distributed systems using Java.
Strong knowledge in Core Java (Java 21+), Spring Boot, React, REST, Kafka, Microservices, Agile, DevOps.
Hands-on practical experience using AI coding assistants (GitHub Copilot, Cursor, Claude, etc.).
Hands-on practical experience delivering system design, application development, testing, and operational stability.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Experience in object oriented analysis and design (OOAD).
Proficiency in automation and continuous delivery methods.
Proficient in all aspects of the Software Development Life Cycle.
Experience working with cloud platforms such as AWS.
Experience on asynchronous communication using messaging/streaming systems (Kafka, RabbitMQ, or equivalents).
Ability to design and optimize data models either in relational or NoSQL databases.
Ability to handle data consistency, replication, sharing, and transactional boundaries.
Familiar with containerization and orchestration tools: Docker, Kubernetes.
Knowledge on integration with CI/CD pipelines for automated build, test, and deployment.
Exposure to cloud technologies.

Nice To Haves

Advanced knowledge of application, data, and infrastructure architecture disciplines.
Strong written and oral communication and excellent presentation and influencing skills.
Familiarity with modern front-end technologies.
Practical cloud native experience.

Responsibilities

Own end-to-end delivery of complex backend components, embedding AI-native capabilities (agentic workflows, LLM-powered features) into core product experiences.
Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems.
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.
Participate actively in design reviews and architecture discussions.
Identify performance bottlenecks and propose optimizations.
Mentor junior engineers through code reviews and technical guidance.