Lead Software Engineer
JPMorgan Chase & Co. (company site, opens in a new tab)5+ yrsHyderabadOn-siteDirect career pageEst. 25–40 LPA
Posted 1d ago
Reach people at JPMorgan Chase & Co.
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at JPMorgan Chase & Co. you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Build and deliver secure, high-quality software for the Deposits 2.0 technology team, including system design, development, testing, and production troubleshooting.
- Improve operational stability by automating recurring remediation and applying resiliency, security, and continuous-delivery practices.
- Lead team adoption of approved AI-assisted engineering tools, set standards for validating outputs, and promote effective patterns; also guide technical evaluations and engineering communities of practice.
Minimum Requirements
- Work Experience Required: 5+ years of applied software engineering experience.
- Formal training or certification in software engineering concepts.
- Advanced Java and AWS skills; hands-on experience with system design, application development, testing, and operational stability.
- Proficiency across the SDLC, automation and continuous delivery, and agile practices including CI/CD, application resiliency, and security; experience guiding safe, compliant use and validation of approved AI-assisted development tools.
Ideal Candidate Profile
- A hands-on technical lead who can contribute across design, coding, testing, debugging, and production stability—not only guide others.
- Has experience setting team practices for secure, responsible AI-assisted engineering, including validating correctness, performance, and security of outputs.
- Can influence engineering outcomes beyond their immediate work through technical evaluations, reusable practices, and communities of practice.