JPMorgan Chase—Software Engineer III - Java Full Stack Developer + GenAI / LLM + AWS

Job description

Description: 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 Full Stack Developer + GenAI / LLM + AWS at JPMorgan Chase within the Commercial & Investment Bank, 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 - Design and develop full-stack software solutions using modern engineering approaches and patterns. - Build and integrate AI-driven capabilities, including LLM-based services, orchestration, and workflow integrations. - Develop and maintain cloud-native microservices and APIs (REST/streaming) with strong focus on scalability, resilience, and security controls. - Identify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing). - Collaborate in a Scrum/Agile team, participate in ceremonies, and contribute to a culture of diversity, opportunity, and inclusion. - Implement solutions primarily using Java, Spring Boot, and Python (AWS Lambda), building microservices and Camunda workflow orchestration deployed on AWS ECS, backed by PostgreSQL. - Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs. - Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations. - Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards. - 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 - Formal training or certification on software engineering concepts and 3+ years applied experience. - Strong application development skills with exposure to operational stability in production environments and hands-on experience in Java Full Stack Development. - Experience with system design fundamentals, microservices patterns, and API development (RESTful and/or streaming). - Experience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure) - Proficiency with data technologies (relational and/or NoSQL) and common observability practices. - Practical familiarity with LLMs / generative AI concepts and use cases (e.g., RAG, tool/prompt orchestration, guardrails/evaluation); working knowledge of Python for AI/ML integrations. - Overall knowledge of the Software Development Life Cycle, and solid understanding of agile methodologies such as CI/CD, Application Resiliency, and Security. - Familiarity with Docker, Kubernetes, Helm, modern CI/CD practices, multi-region service deployments, and zero-downtime release strategies. - Strong communication skills, ownership mindset, proactive approach to continuous improvement, and a track record delivering scalable, reliable, and secure products from concept to launch. - Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, testing, troubleshooting, or documentation) with demonstrated ability to critically evaluate and validate AI-generated outputs. - Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations. Preferred qualifications, capabilities, and skills - Cloud certification in AWS, GCP, or Azure. - Working knowledge of Python (for AI/ML integrations) is a plus. - Familiarity with Docker, Kubernetes, Helm, and modern CI/CD practices. - Experience with multi-region service deployments and zero-downtime release strategies. - Strong communication skills, ownership mindset, and a proactive approach to continuous improvement. - Track record delivering scalable, reliable, and secure products from concept to launch. - Working knowledge of Python (for AI/ML integrations) is a plus.