Job Description
Are you passionate about building the cloud infrastructure that powers one of the world's most influential financial institutions? At JPMorganChase, we invest in our engineers and give you the tools, scale, and autonomy to solve complex problems that matter. Here, your work doesn't just support a product — it shapes the foundation that thousands of teams rely on every day. Join a culture that values innovation, inclusion, and continuous growth, where your ideas are heard and your contributions make a measurable difference.
As a Lead Software Engineer at JPMorganChase within the Cloud Foundational Services team, you will be a pivotal contributor on an agile team committed to enhancing, developing, and delivering high-quality technology products in a secure, stable, and scalable way. You will apply your deep technical expertise in cloud-native technologies and Kubernetes to solve complex challenges across a broad range of applications and platforms. Your contributions will directly influence product design, engineering practices, and the cloud infrastructure that underpins the firm's technology ecosystem.
Job responsibilities
- Develop secure, high-quality production code and conduct thorough code reviews and debugging to uphold engineering excellence across the team
- Drive technical decisions that shape product design, application functionality, and operational processes at scale
- Execute software solutions across design, development, and troubleshooting, leveraging Kubernetes and cloud-native experience to move beyond conventional approaches
- Collaborate with cross-functional cloud platform engineering teams to deliver secure, scalable, and resilient applications on the cloud
- Influence peers and project decision-makers to adopt and apply leading-edge cloud technologies and engineering practices
- Advocate for firmwide frameworks, tools, and Software Development Life Cycle best practices within the engineering community
- Ensure cloud solutions meet security and regulatory compliance requirements, embedding controls throughout the development lifecycle
- Stay current with advancements in cloud technologies and provide recommendations for the adoption and implementation of new tools and approaches
- 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
- Contribute to a team culture that champions diversity, opportunity, inclusion, and respect
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Proficiency in one or more programming languages, with demonstrated experience in Golang
- Advanced knowledge of software applications and technical processes, with considerable depth in cloud, distributed systems, or a related technical discipline
- Practical, hands-on experience with cloud-native technologies, including Kubernetes and its surrounding ecosystem
- Experience with Infrastructure as Code tools such as Terraform for provisioning and managing cloud infrastructure
- Experience developing, debugging, and maintaining code in a large-scale environment using modern programming languages and database querying languages
- Solid understanding of agile methodologies, including continuous integration and delivery, application resiliency, and security practices
- Ability to independently tackle design and functionality challenges with minimal oversight
- 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
- AWS Professional certification
- Kubernetes Certified Application Developer (CKAD) certification
- Experience managing a large fleet of Kubernetes clusters in an enterprise or production environment
- Familiarity with virtual cluster platforms such as vCluster
- Background in Computer Science, Computer Engineering, Mathematics, or a related technical field