Software Engineer Iii-devops, Aws

JPMorgan Chase JPMorgan Chase · Banking · Bengaluru, Karnataka, India · Consumer & Community Banking

Software Engineer III-Devops, AWS role at JPMorgan Chase focuses on designing and delivering technology products within the API Marketplace. The role involves executing software solutions, creating secure code, producing architecture artifacts, and leveraging enterprise-authorized AI coding assist tools to improve productivity. It also requires gathering and analyzing data for continuous improvement, identifying problems in data, contributing to software engineering communities, and fostering a diverse team culture. Key requirements include experience in system design, application development, cloud-native solutions, DevOps practices, Kubernetes, CI/CD, AWS, SQL, Python, and AI-assisted development tools, with an understanding of responsible AI use.

What you'd actually do

  1. Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems
  2. Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
  3. Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
  4. Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
  5. 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.

Skills

Required

  • Formal training or certification on software engineering concepts and 3+ years applied experience
  • Formal training or certification on software engineering concepts and 5+ years applied experience.
  • Hands-on practical experience delivering system design, application development, testing, and operational stability.
  • Practical experience building and operating cloud-native solutions.
  • Proficiency with DevOps practices, Kubernetes, CI/CD, and AWS.
  • Experience writing SQL queries for analysis and troubleshooting.
  • Experience in one or more programming languages, including Python.
  • Proficiency in automation and continuous delivery methods to improve engineering throughput and quality.
  • Proficiency across all phases of the Software Development Life Cycle (SDLC).
  • Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Nice to have

  • Experience with monitoring/observability tools such as Grafana and Dynatrace.
  • Experience running Kubernetes on AWS, including Amazon EKS.

What the JD emphasized

  • Hands-on practical experience delivering system design, application development, testing, and operational stability.
  • Practical experience building and operating cloud-native solutions.
  • Proficiency with DevOps practices, Kubernetes, CI/CD, and AWS.
  • Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
  • Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.