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JPMorganChase

Lead Software Engineer - Terraform, Python, Kubernetes

Bangalore, Karnataka, India

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hirly's read of this role

Role family
Engineering
Seniority
Lead / management
Country
IN
Work mode
On-site / unstated
First seen by hirly
23 Sept 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

  • Description
  • 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, Securities Services Technology, 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 breakdown technical problems
  • Develops secure and high-quality production code, and reviews and debugs code written by others
  • 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.
  • 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
  • Required qualifications, capabilities, and skills
  • Formal training or certification on software engineering concepts and 5+ years applied experience.
  • Extensive experience in building and running AWS/public cloud based applications
  • Solid programming skills with Python
  • Hands-on practical experience delivering system design, application development, testing, and operational stability
  • Proficiency in automation and continuous delivery methods and all aspects of the Software Development Life Cycle
  • Experience of pipelines and DAG's (Directed Acyclic graph) for processing data and/or machine learning
  • Demonstrated proficiency in software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence and machine learning.)
  • In-depth knowledge of the financial services industry and their IT system.
  • 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 Cloud services, Infrastructure as Code (IaC) and containerized application development
  • Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock
  • Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps)
  • Experience working on AIML systems and/or prior experience collaborating with data scientists
Original posting on JPMorganChase's site ↗

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