at J.P. Morgan
Bulge Bracket Investment BanksPosted 12 days ago
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**Lead Software Engineer (Java Full Stack, Python, AI) - Bengaluru** - Lead agile teams building market-leading tech products. Own features from concept to delivery, enhancing security, scalability, and stability. - Expertise in full stack development: Java, JavaScript, Python, along with front-end (responsive UI, usability, accessibility) and back-end (APIs, cloud services) development. - Hands-on cloud and DevOps: Contribute to infrastructure, CI/CD, automation, and monitoring using modern delivery practices. - Ensure quality and reliability through rigorous testing, code review, observability, and incident readiness. - Foster secure-by-design practices, technical leadership, and enterprise-authorized AI-assisted engineering. - 5+ years of software engineering experience with strong communication skills and adaptability in ambiguous environments.
- Compensation
- Not specified
- City
- Bengaluru
- Country
- India
Currency: Not specified
Full Job Description
Location: Bengaluru, Karnataka, India
As a Lead Software Engineer at JPMorganChase within the Asset and Wealth Management, 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 firms business objectives.
Job responsibilities
- Full stack delivery: Own features end-to-end from requirements and design through implementation, testing, deployment, and ongoing support.
- Front-end development: Build responsive, maintainable user interfaces with strong attention to usability, accessibility, and performance.
- Back-end development: Develop scalable services and APIs, including data access, business logic, and integration with upstream/downstream systems.
- Cloud & DevOps: Contribute hands-on to cloud infrastructure, CI/CD pipelines, automation, monitoring, and operational readiness.
- Quality & reliability: Apply best practices in testing, code review, observability, performance tuning, and incident readiness.
- Security & controls: Build with secure-by-design practices (access control, secrets management, auditability, responsible data handling).
- Technical leadership (IC): Influence architecture and standards, mentor engineers, and lead by example without formal people management.
- 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.
- Formal training or certification on software engineering concepts and 5+ years applied experience
- Strong proficiency in Java, JavaScript, and Python, plus experience with other modern languages/frameworks as needed.
- Proven experience building both front-end applications and back-end services/APIs.
- Experience with cloud platforms and modern delivery practices (automation, CI/CD, monitoring/observability).
- Strong engineering discipline across testing, documentation, and maintainable design.
- Excellent communication skills and ability to operate effectively in ambiguous environments.
- 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
