Lead Software Engineer

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

Lead Software Engineer role focused on enabling the Gen AI platform and developing Gen AI use cases, including LLM fine-tuning and multi-agent orchestration. The role involves driving team adoption of AI-assisted engineering practices and ensuring responsible AI use within engineering workflows.

What you'd actually do

  1. 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.
  2. Executes using Platform engineering to enable the Gen AI platform and develop the Gen AI Use cases ,LLM fine tuning and multi agent orchestration.
  3. Hands on code development to enable our AI/ML platform, ensuring robustness, scalability, and high performance.
  4. 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.
  5. Writes secure and high-quality code using the syntax of at least one programming language with limited guidance

Skills

Required

  • Formal training or certification on software engineering concepts and 5 + years of applied experience
  • Extensive practical experience with Python and AWS cloud services, including EKS, EMR, ECS,
  • Hands-on experience in AIML lifecycle development.
  • Advanced knowledge in Generative AI, Agent development and the AI platform engineering

Nice to have

  • AI-assisted code review/refactoring
  • test strategy acceleration
  • incident/root-cause analysis support
  • secure coding
  • peer review
  • automated testing
  • LLM fine tuning
  • multi agent orchestration

What the JD emphasized

  • 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

Other signals

  • AI-assisted engineering practices
  • Gen AI platform
  • LLM fine tuning
  • multi agent orchestration
  • Agent development