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Salary
≈ $34k – $85k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Staff · 5+ years exp

Confirmed on the employer's own hiring board on Nov 4, 2026. First seen by Alion on Sep 8, 2026. JPMorganChase scores A on the Alion truth index.

Overview
Company
Impact
Profile match
JPMorganChase is the largest bank in the United States by assets and one of the most systemically important financial institutions in the world, with a lineage running back through more than a thousand predecessor firms to the 1799 founding of the Bank of the Manhattan Company. It combines a dominant investment bank and markets business with Chase, the largest retail banking franchise in America, plus commercial banking and asset and wealth management. Headquartered in New York, the group is unusual among banks for the scale of its technology spending, running one of the largest engineering organisations of any financial institution and deploying its own internal AI platform across the firm.

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, youare 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

  • Designs, develop, test, and deploy scalable full-stack applications using .NET and React
  • Builds and maintain RESTful APIs and backend services using C# and ASP.NET Core
  • Develops responsive, user-friendly front-end interfaces using React, TypeScript, HTML, and CSS
  • Implements cloud-native solutions on AWS (compute, storage, networking, monitoring, security)
  • Automates infrastructure provisioning and environment management using Terraform. Troubleshoots and resolve production issues across application and cloud infrastructure layers
  • Writes secure, clean, maintainable, and well-documented production code. Contributes to architecture, design reviews, and technical decision-making
  • Collaborates with cross-functional teams (product, QA, DevOps, security) in agile sprints
  • Supports CI/CD pipelines and improve release reliability, performance, and resiliency
  • 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
  • Hands-on experience in full-stack application development and system design
  • Strong proficiency in C#, .NET, and ASP.NET Core
  • Strong front-end experience with React (with TypeScript). Practical experience with AWS services and cloud deployment patterns
  • Hands-on Terraform experience for Infrastructure as Code. Experience with relational and/or NoSQL databases and query optimization
  • Strong debugging, testing, and performance tuning skills
  • Understanding of SDLC, secure coding, and agile development practices
  • Familiarity with CI/CD, application resiliency, and observability best practices
  • 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

Preferred qualifications, capabilities, and skills

  • Experience with containerization and orchestration (Docker, Kubernetes, ECS/EKS)
  • Knowledge of microservices and event-driven architecture
  • Familiarity with automated testing frameworks and test strategy
  • Experience with monitoring/logging tools (CloudWatch, ELK, Datadog, etc.). Exposure to enterprise-scale distributed systems
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