Lead Software Engineer- AI Platform Engineer

JPMorgan Chase JPMorgan Chase · Banking · San Francisco, CA +1 · Corporate Sector

Lead Software Engineer for an AI Platform team at JPMorgan Chase, focusing on building and enhancing infrastructure solutions for AI/ML workloads. The role involves designing, building, and troubleshooting technical challenges, writing production code, automating processes, and collaborating with AI teams to translate computational requirements into effective infrastructure. Key responsibilities include designing CI/CD pipelines for ML, developing automation scripts, driving adoption of AI-assisted engineering practices, and ensuring responsible AI use in engineering workflows.

What you'd actually do

  1. Collaborate with AI teams to translate computational requirements into effective infrastructure solutions.
  2. Design and implement continuous integration and delivery (CI/CD) pipelines for machine learning workloads.
  3. Develop automation scripts and use infrastructure as code to streamline deployment and management processes.
  4. 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.
  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 in software engineering and at least 5 years of relevant experience.
  • Proven experience in system design, application development, testing, and maintaining operational stability.
  • Advanced proficiency in one or more programming languages such as Go or Java.
  • Experience with Kubernetes and containerization technologies (e.g., Docker).
  • Ability to independently address design and functionality challenges with minimal supervision.
  • Experience with infrastructure as code tools such as Terraform or Ansible.
  • Solid understanding of cloud architecture, including microservices, IaaS, storage, security, and networking concepts.
  • 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

Nice to have

  • Basic understanding of NVIDIA GPU infrastructure software (e.g., DCGM, BCM, Dynamo Inference).
  • Familiarity with observability tools such as Prometheus and Grafana.
  • Experience working with public cloud platforms such as AWS or GCP.

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
  • infrastructure for ML workloads
  • responsible AI use