Senior Lead Software Engineer- AI Platform engineer

JPMorgan Chase & Co. • Seattle, WA

3d • $175,750 - $260,000

About The Position

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Sector, Infrastructure Platforms team, you will contribute to an agile team building and enhancing secure, stable, scalable technology products. You will apply deep technical expertise and problem-solving skills to challenges across multiple technologies and applications, and drive business impact through your contributions.

The role focuses on architecting and deploying cloud platforms optimized for AI/ML workloads, translating AI teams’ compute needs into infrastructure requirements, and optimizing cloud resources for performance and cost. You will build CI/CD, automation, and infrastructure-as-code capabilities for ML deployment and operations. You will also lead adoption and governance of approved AI-assisted engineering practices across teams, establish measurable validation standards, and encourage reuse of proven patterns and automation within the SDLC/TLM toolchain.

The position is part of Global Technology Infrastructure, which develops technology products and solutions in a stable, resilient, and secure operating environment. The position is subject to Section 19 of the Federal Deposit Insurance Act, and an employment offer is contingent on review of criminal conviction history, including pretrial diversions or program entries.

Requirements

• Formal training or certification in software engineering concepts and 5+ years of applied experience.
• Hands-on experience in system design, application development, testing, and operational stability.
• Strong experience with Kubernetes and containerization (Docker), including cluster operations and production troubleshooting.
• Proficiency in at least one programming language, such as Python, Go, Java, or C#.
• Ability to independently tackle design and functionality problems with minimal oversight.
• Strong knowledge of cloud computing delivery models (IaaS, PaaS, SaaS) and deployment models (Public, Private, Hybrid Cloud).
• Foundational understanding of machine learning concepts, including transformer architecture, ML training, and inference.
• Experience with Infrastructure as Code.
• Deep understanding of cloud component architecture, including microservices, IaaS, storage, security, and routing/switching technologies.
• Demonstrated experience leading effective use of enterprise-authorized AI-assisted software development tools, 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, secure handling of inputs and outputs, and resiliency and security expectations.
• Experience coaching senior engineers or leads on compliant AI usage patterns and controls.

Nice To Haves

• Foundational understanding of NVIDIA GPU infrastructure software, such as DCGM, BCM, and Dynamo Inference.
• Proficiency with observability tools such as Prometheus and Grafana.
• Experience in ML Ops and related tooling, including MLflow.
• Background in high-performance computing and ML frameworks such as vLLM, Ray.io, and Slurm.
• Strong knowledge of network architecture, database programming (SQL/NoSQL), and data modeling.
• Familiarity with cloud data services, big data processing tools, and Linux environments.

Responsibilities

• Provide technical guidance aligned to business goals, collaborating with engineers, contractors, and vendors.
• Develop secure, high-quality production code; review, debug, and improve others’ code.
• Influence product design, application functionality, and technical operations through informed decisions.
• Champion firmwide SDLC frameworks, tools, and best practices.
• Promote a culture of diversity, opportunity, inclusion, and respect.
• Architect and deploy secure, scalable cloud platforms optimized for AI/ML workloads.
• Partner with AI teams to translate compute needs into infrastructure requirements.
• Monitor, manage, and optimize cloud resources for performance and cost efficiency.
• Build CI/CD, automation, and infrastructure-as-code to streamline ML deployment and operations.
• Drive adoption and governance of approved AI-assisted engineering practices across teams to improve code quality, delivery speed, and operational outcomes.
• Establish measurable validation standards for AI-assisted engineering, including secure coding, peer review, and automated testing.
• Promote reuse of proven patterns and automation within the SDLC/TLM toolchain.
• Apply knowledge of SDLC toolchain capabilities, including approved AI-assisted development and automation tools, to improve the value realized by automation at scale.

Benefits

• Competitive total rewards package, including base salary determined by role, experience, skill set, and location.
• Eligible roles may receive commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed