Lead Software Engineer

JPMorganChase · Bengaluru · 5+ yrs experience · Posted 2026-07-21

Tech stack: AWS, Java, Kafka, React, Redis, Spring Boot, Terraform, TypeScript

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About the role

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.
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.
Qualifications:
- 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

Qualifications

- 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

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.

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