Lead Software Engineer-Golang
JPMorgan Chase · Bangalore, India · 20h ago
Staff / Lead / PrincipalOn-siteSoftware Engineering
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As a Lead Software Engineer at JPMorgan Chase within the Infrastructure Platforms team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
As a Lead Software Engineer at JPMorgan Chase within the Infrastructure Platforms team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
- Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
- Build and operate cloud-native solutions using Kubernetes with strong systems fundamentals, on at least one cloud provider (AWS / GCP / Azure)
- Develop services primarily in Golang and/or Java (either or both)
- Produce secure, high-quality production code aligned to engineering best practices
- Review and debug code written by others while enforcing consistent validation standards (secure coding, peer review, automated testing)
- Drive consistent validation practices across the team to improve overall code quality and delivery outcomes
- Promote reuse of effective patterns and scalable approaches across the team to improve engineering efficiency
- Identify and implement opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability, and lead evaluation sessions with external vendors/startups and internal teams to assess architectural designs and technical fit within existing systems and information architecture
- 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
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 5+ years applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s)
- Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
- Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- In-depth knowledge of the financial services industry and their IT systems
- Practical cloud native experience
Preferred qualifications, capabilities, and skills
- Spec Drive Development (SDD) experience is a plus
- Strong AI tool usage fluency (comfortable leveraging AI tools in day-to-day engineering workflows)
- Prior understanding of Cloud Governance is a plus