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JPMorganChase

Lead Software Engineer - Java

Reposted 2 Hours Ago
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Hybrid
Mumbai, Maharashtra
Senior level
Hybrid
Mumbai, Maharashtra
Senior level
Lead an agile team to design, develop, and deliver secure, scalable Java backend microservices. Drive architecture reviews, vendor evaluations, CI/CD, automation, production troubleshooting, and adoption of enterprise-authorized AI-assisted engineering practices while ensuring security and operational resiliency.
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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 JPMorganChase within the Commercial & Investment Bank, 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 firm’s business objectives.

Job responsibilities

 

  • Executes creative software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  • Develops secure high-quality production code, and reviews and debugs code written by others
  • Identifies opportunities to eliminate or automate remediation of recurring issues to improve overall operational stability of software applications and systems
  • Leads evaluation sessions with external vendors, startups, and internal teams to drive outcomes-oriented probing of architectural designs, technical credentials, and applicability for use within existing systems and information architecture
  • Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies
  • Adds to team culture of diversity, opportunity, inclusion, 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

  • Strong Java backend microservices (Java 11/17), Spring Boot, REST/gRPC, security (OAuth2/JWT)
  • Java + Spring Boot microservices, solid testing (JUnit/Mockito), performance basics. Kafka/MSK working experience in production
  • AWS deployment familiarity + troubleshooting (not necessarily deep networking design)
  • Docker + basic Kubernetes usage. Proficiency in automation and continuous delivery methods
  • Proficient in all aspects of the Software Development Life Cycle. Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
  • Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)
  • In-depth knowledge of the financial services industry and their IT systems. Practical cloud native experience
  • 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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