Lead Software Engineer - Java, AI
JPMorgan Chase · Glasgow, United Kingdom · 22d ago
Join JPMorganChase, where technology powers the future of global finance. As part of our Corporate Technology organization, you'll work alongside some of the brightest engineering minds in the industry, building resilient, scalable systems that serve millions of customers and businesses worldwide. Here, your work doesn't just solve technical problems — it drives real business outcomes at extraordinary scale. We invest in your growth, champion collaboration, and give you the tools to do the best work of your career.
As a Lead Software Engineer at JPMorganChase within Corporate Technology, you will play a pivotal role in designing and delivering high-quality software solutions that power critical enterprise platforms. You will bring technical depth, a collaborative mindset, and a passion for engineering excellence to a team that values innovation and continuous improvement. Your contributions will directly influence the reliability, scalability, and performance of systems that matter to the firm and its clients.
Job responsibilities
- Design and develop robust, scalable Java-based applications that meet complex enterprise requirements across multiple business domains
- Lead the architecture and implementation of cloud-native solutions on AWS, ensuring high availability, security, and performance
- Drive the adoption and continuous improvement of CI/CD pipelines, enabling faster, more reliable software delivery
- Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team
- 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
- Collaborate with cross-functional teams including product, architecture, and business stakeholders to translate requirements into technical solutions
- Conduct thorough code reviews and establish engineering best practices that elevate the quality and consistency of the team's output
- Mentor and coach junior engineers, fostering a culture of learning, ownership, and technical growth
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Advanced proficiency in Java, including experience building and maintaining large-scale, production-grade applications
- Hands-on experience designing and deploying cloud-native solutions on AWS (e.g., EC2, S3, Lambda, RDS, EKS)
- Demonstrated experience building and maintaining CI/CD pipelines using industry-standard tools
- Strong understanding of software design patterns, distributed systems, and microservices architecture
- Proven ability to lead technical discussions, influence design decisions, and drive alignment across engineering teams
- 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 infrastructure-as-code tools such as Terraform or AWS CloudFormation
- Familiarity with containerization and orchestration technologies such as Docker and Kubernetes
- Exposure to observability and monitoring practices using tools such as Datadog, Splunk, or equivalent platforms
- Experience contributing to or leading architectural decisions in a large, matrixed enterprise environment
- Knowledge of security best practices in cloud and application development contexts