Lead Software Engineer- AI Platform Engineer

JPMorgan Chase & Co.•San Francisco, CA

15h•$152,000 - $215,000

About The Position

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 JP Morgan Chase within the Corporate Sector, Infrastructure Platforms team, 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.

Requirements

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 Haves

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.

Responsibilities

Develop creative software solutions by designing, building, and troubleshooting technical challenges, applying innovative approaches to solve complex problems.
Write secure, high-quality production code and conduct code reviews and debugging for team members.
Identify and automate remediation of recurring issues to enhance the operational stability of software applications and systems.
Collaborate with AI teams to translate computational requirements into effective infrastructure solutions.
Monitor, manage, and optimize cloud resources to ensure performance and cost efficiency.
Design and implement continuous integration and delivery (CI/CD) pipelines for machine learning workloads.
Develop automation scripts and use infrastructure as code to streamline deployment and management processes.
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.