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Software Engineer III - Java Developer + AI + LLM
at JP Morgan Chase & Co. · Information Technology
Mumbai
On-site
0+ years
18 days ago
We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.
As a Software Engineer III - Java Developer + AI + LLM at JPMorgan Chase within the Commercial & Investment Bank - Global Banking, you'll serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
As a Software Engineer III - Java Developer + AI + LLM at JPMorgan Chase within the Commercial & Investment Bank - Global Banking, you'll serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives.
Job responsibilities
- Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Develops secure high-quality production code primarily Java / Spring Boot/ Microservices/APIs within the Spring ecosystem (e.g., Spring MVC/WebFlux, Spring Data, Spring Security) with strong testing practices, reviews and debugs code written by others
- Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
- Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards contributes learnings and reusable patterns to improve broader team effectiveness.
- 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.
Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 3+ years applied experience
- 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, JPA persistence), Apache Kafka, Elastic search, plus unit/integration testing.
- Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
- 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
- Overall knowledge of the Software Development Life Cycle and Practical experience on Kubernetes
- Solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- 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.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations ability to guide peers on safe and effective usage within team practices.
Preferred qualifications, capabilities, and skills
- Experience with modern Microservice patterns (resiliency, distributed tracing, event-driven design)
- Exposure to cloud technologies
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