Lead Software Engineer - Terraform, Python, Kubernetes
Posted Updated
Summary
Lead Software Engineer in JPMorgan Chase's Securities Services Technology group in Bengaluru, building and delivering secure Python applications on AWS, leading design reviews and a small hands-on team, and integrating AI/ML solutions with Kubernetes, Docker, and infrastructure-as-code tooling.
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan Chase within the Commercial & Investment Bank, Securities Services Technology, 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 Commercial & Investment Bank, Securities Services Technology, 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
- Develops and maintain high-quality, secure applications using Python and AWS
- Creates architecture and design deliverables, lead design and architecture reviews, own end-to-end delivery. Integrates AIML solutions into complex, domain specific operations processing systems
- Leads code reviews, design discussions, and agile planning sessions. Collaborates with SRE and production monitoring teams to ensure system reliability and performance
- Contributes to software engineering communities of practice and technology events
- Embraces continuous learning, creative problem solving, and a can-do attitude
- Leads evaluation and selection of open source and internal software to determine best fit
- Partners with existing systems teams on cross‑app design; align interfaces and ship end‑to‑end solutions
- Manages small team of engineers whilst remaining hands on, setting engineering standards
- 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.
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years applied experience.
- Proven hands-on experience in Python application development
- Proven hands-on experience developing, debugging and maintaining production applications
- Solid understanding of software development best practices, including version control, testing, and CI/CD
- Strong problem-solving, communication, and collaboration skills, with the ability to convey design choices and communicate effectively with stakeholders
- Experience working on AIML systems and/or prior experience collaborating with data scientists
Track record of designing, building, and delivering maintainable, extensible applications into production environments
Preferred qualifications, capabilities, and skills
- Experience with Cloud services, Infrastructure as Code (IaC) and containerized application development
- Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock
- Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and LLMs
- Experience working on AIML systems and/or prior experience collaborating with data scientists