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.