Senior Lead Software Engineer- AI Platform engineer

JPMorganChase•Seattle, WA

About The Position

As a Senior Lead Software Engineer at JPMorgan Chase within the Corporate Sector, Infrastructure Platforms team, you will help an agile team enhance, build, and deliver secure, stable, scalable, and market-leading technology products. You will use deep technical expertise and problem-solving skills to address challenges across technologies and applications and drive business impact. The role focuses on architecting and deploying cloud platforms optimized for AI/ML workloads, translating AI teams’ compute needs into infrastructure requirements, and improving platform performance and cost efficiency. You will build CI/CD, automation, and infrastructure-as-code capabilities to streamline ML deployment and operations. You will also advance the adoption and governance of approved AI-assisted engineering practices across teams, establish standards for validating AI outputs, and promote reusable automation and patterns across the SDLC/TLM toolchain. This position is subject to Section 19 of the Federal Deposit Insurance Act. An employment offer is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.

Requirements

Formal training or certification in software engineering concepts and at least 5 years of applied experience.
Hands-on experience in system design, application development, testing, and operational stability.
Strong experience with Kubernetes and containerization, including Docker, cluster operations, and production troubleshooting.
Proficiency in at least one programming language, such as Python, Go, Java, or C#.
Ability to independently address 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 in the work environment, such as for coding, code review, test acceleration, and troubleshooting.
Ability to set team expectations for validating AI-assisted 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 adherence to 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 MLOps 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 with business goals while collaborating with engineers, contractors, and vendors.
Develop secure, high-quality production code; review, debug, and improve code written by others.
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.
Use knowledge of SDLC toolchain capabilities, including approved AI-assisted development and automation tools, to improve the value realized from automation at scale.
Set team expectations for validating AI-generated outputs for correctness, performance, and security.
Coach senior engineers and leads on compliant AI-tool usage patterns and controls.