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Salary
$35k – $92k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Staff
Overview
Company
Impact
Profile match
JPMorgan Chase & Co. is a leading global financial services firm and the largest banking institution in the United States by assets. Headquartered in New York City, the company offers a comprehensive range of financial solutions, including investment banking, asset management, treasury services, and commercial banking. Through its widely recognized consumer division, Chase, it delivers retail banking, credit card, and mortgage services to tens of millions of households across the globe.

We offer an opportunity to accelerate your career through challenging, high-impact work where you can push the boundaries of what’s possible.

As a Lead Software Engineer at JPMorgan Chase within Consumer and Community Banking Digital Channels Public, you will be a key member of an agile team delivering secure, stable, and scalable technology products. As a core technical contributor and people leader, you will drive critical solutions across multiple technical areas and business functions in support of the firm’s objectives.

Job Responsibilities

  • Design and deliver innovative software solutions, including complex troubleshooting that goes beyond standard approaches.
  • Build secure, high-quality production code; review, debug, and improve code written by others.
  • Identify and automate remediation of recurring issues to improve operational stability and resiliency.
  • Lead evaluation sessions with external vendors, startups, and internal teams to assess architecture, technical fit, and alignment with enterprise information architecture.
  • Lead and contribute to engineering communities of practice to promote adoption of modern and leading-edge technologies.
  • Foster a team culture rooted in diversity, inclusion, opportunity, and respect.
  • 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 with system design, application development, testing, and operational stability.
  • Advanced proficiency in one or more programming languages.
  • Hands on experience in Java: Core Java, concurrency, JVM fundamentals, clean code, design patterns.
  • Strong experience in Spring Boot: REST APIs, Spring Data JPA, transactions, validation, OAuth2/JWT security, testing (JUnit, Mockito, Testcontainers).
  • Proficient in AWS: IAM/KMS, VPC fundamentals, ECS/EKS or Lambda, S3, RDS/Aurora, DynamoDB/Redis (as needed), CloudWatch, IaC (Terraform/CloudFormation), CI/CD.
  • React (TypeScript): hooks, component architecture, state management, routing, API integration/auth, testing (RTL/Jest, Cypress/Playwright).
  • Agentic AI: tool/function calling, RAG fundamentals, agent patterns (planner-executor/ReAct), evaluation/monitoring, prompt-injection defense, PII safeguards.
  • Engineering maturity: microservices, messaging (Kafka/SQS), observability (logs/metrics/tracing), SLOs/on-call, design docs, mentoring.
  • 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
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