at J.P. Morgan
Bulge Bracket Investment BanksPosted 8 days ago
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**Senior Java Software Engineer III** Design and own backend services for complex, high-throughput systems in Glasgow, Lanarkshire, UK. As a seasoned Java engineer with 8+ years of experience, you'll engineer scalable Spring Boot microservices, following cloud-native practices on AWS or Cloud Foundry. Apply AI-enabled developer tools responsibly to enhance productivity and code quality, partnering with stakeholders to drive business outcomes in an Agile environment. Key skills required: Java 21 (Java 8 and higher), Spring Boot, Apache Kafka, Cassandra (NoSQL), and React JS.
- Compensation
- Not specified
- City
- Not specified
- Country
- United Kingdom
Currency: Not specified
Full Job Description
Location: GLASGOW, LANARKSHIRE, United Kingdom
Join a market-leading Margin & Collateral Services team within the CIB (Commercial & Investment Bank) and build modern, cloud-ready platforms that power critical posttrade and collateral workflows. Were looking for a high-impact, deeply hands-on, and delivery-focused Java engineer with 8+ years of experience who thrives in complex domains, loves turning ambiguity into elegant solutions, and enjoys partnering with stakeholders to drive meaningful business outcomes.
Youll work in an Agile environment, using modern engineering practices and AI-enabled developer tools to accelerate delivery and improve quality, while applying cost-effective & responsible AI practices throughout.
Job responsibilities
- Design, build, and own backend services for complex, high-throughput systems.
- Partner with product, operations, and technology stakeholders to clarify requirements, shape solutions, and influence decisions through strong communication.
- Engineer scalable, resilient Spring Boot microservices with a focus on performance, stability, observability, and operability.
- Lead architecture and engineering best practices: clean code, automated testing, code reviews, CI/CD, and continuous improvement.
- Apply AI-enabled developer tools responsibly to enhance engineering productivity and quality (e.g., accelerating coding, testing, documentation, and analysis workflows).
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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience.
- Strong Java expertise through Java 21, including modern language features and best practices, building and designing complex applications.
- Extensive experience building Spring Boot microservices and designing for scalability, resilience, and maintainability.
- Experience working in modern distributed systems environments, with exposure to technologies such as Apache Kafka and Cassandra (NoSQL); plus working knowledge of React JS.
- Hands-on experience with cloud platforms (e.g., AWS, Cloud Foundry) and cloud-native engineering practices.
- Strong problem-solving skills with ability to translate ambiguous business requirements into high-quality technical solutions.
- Excellent communication and stakeholder management skills, including influencing across teams to drive optimal outcomes.
- Passion for applying AI to software engineering workflows, with awareness of responsible usage and risk considerations.
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.
