Software Engineer III - Trading Platform Development

JPMorgan Chase & Co.•Jersey City, NJ

8d•$137,750 - $185,000

About The Position

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the Asset & Wealth Management Technology organization, you 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. Join a team at the forefront of trading technology innovation. You’ll work in a collaborative environment where your contributions directly support the success of our multi-asset trading platform.

Requirements

Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience in system design, application development, testing, and operational stability
Hands-on practical experience in core Java across the Software Development Life Cycle, including system design, application development, testing, CI/CD pipelines, and operational support.
Experience with messaging and streaming technologies such as Kafka, IBM MQ, Rabbit MQ, MSK...
Working knowledge of SQL and NoSQL databases, and event-driven architectures.
Experience with caching systems like Redis, GemFire, or Apache Ignite
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.
Experience with distributed systems concepts and building services in patterns such as microservices and RESTful APIs.
Practical understanding of DevOps toolchains, and agile, test-driven delivery.
Hands-on exposure to AWS cloud services and ecosystems

Nice To Haves

Familiarity with modern front-end technologies (React)
Familiarity with trading systems; exposure to the FIX protocol is a plus.
Ability to solve complex, mission-critical problems with a pragmatic, hands-on approach.
Continuous learner and early adopter of emerging technologies and methodologies.

Responsibilities

Collaborate with engineers to design, build, and deploy applications aligned with business and functional requirements.
Contribute to Java-based microservices using streaming, non-blocking, and reactive programming paradigms; write production-quality code.
Work with Kafka, IBM MQ, Kubernetes (K8s), Redis, Amazon MSK, and AWS services to deliver scalable, efficient systems.
Support domain-driven design adoption and participate in architectural discussions.
Contribute to technical documentation and service APIs (Swagger/OpenAPI), ensuring strong logging, auditability, security, and monitoring.
Participate in API design sessions and help select appropriate design patterns and integration approaches.
Engage in code reviews; promote TDD/BDD practices to ensure maintainability and quality.
Assist in demonstrating service scalability and resilience using mock frameworks, performance testing, and related tools.
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.