Senior Lead Software Engineer, AI Platform Engineer

JPMorgan Chase

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Quick summary

Work type: On-site
Location: Seattle, WA
Posted: 3 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $187k

$119k most similar roles pay here $247k

This listing doesn't post a salary. Most similar roles pay $151,000–$223,750.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1138 open roles on FindRole.

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At a glance

TL;DR · Senior Lead Software Engineer, AI Platform Engineer

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As a Senior Lead Software Engineer- AI Platform engineer within the Infrastructure Platforms team, you will join an agile team focused on building and delivering secure, stable, and scalable technology products. You will architect and deploy cloud platforms optimized for AI/ML workloads, translate compute needs into infrastructure requirements, and manage cloud resources for performance and cost efficiency. Your daily work involves developing production code, managing CI/CD pipelines, and implementing infrastructure-as-code to streamline machine learning operations. You will utilize technologies including Kubernetes, Docker, Python, Go, Java, or C#, along with tools like Prometheus, Grafana, MLflow, vLLM, and Ray.io. The role addresses the technical challenge of scaling AI-assisted engineering practices while ensuring secure coding standards, robust infrastructure for transformer architectures, and high-performance computing in a complex corporate environment.

Skills

Kubernetes Docker Python Go Java C# Infrastructure as Code CI/CD Cloud Computing Microservices ML Ops Prometheus Grafana MLflow vLLM Ray.io Slurm SQL NoSQL Linux

What you'll do

Develop secure, high-quality production code and perform peer code reviews to ensure quality.
Architect and deploy scalable cloud platforms optimized specifically for AI/ML workloads.
Translate machine learning compute requirements into specific infrastructure requirements in partnership with AI teams.
Build CI/CD pipelines and Infrastructure as Code (IaC) to automate ML deployment and operations.
Monitor and optimize cloud resources to ensure high performance and cost efficiency.
Drive the adoption of approved AI-assisted engineering practices to improve code quality and delivery speed.
Establish measurable validation standards for secure coding, automated testing, and peer review processes.
Provide technical guidance and influence product design based on business goals and technical expertise.

What we're looking for

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#.
Knowledge of cloud computing delivery models and infrastructure components like microservices, storage, and networking.
Experience with Infrastructure as Code and foundational understanding of machine learning concepts.
Demonstrated experience leading the use of enterprise-authorized AI-assisted software development tools in engineering workflows.
Understanding of responsible AI usage, including data sensitivity and secure handling of inputs/outputs.
Foundational understanding of NVIDIA GPU infrastructure software (preferred).
Proficiency with observability tools like Prometheus and Grafana (preferred).
Experience in ML Ops and related tooling, such as MLflow (preferred).
Background in high performance computing and ML frameworks like vLLM or Ray.io (preferred).
Strong knowledge of network architecture, database programming, and data modeling (preferred).
Familiarity with cloud data services, big data processing tools, and Linux environments (preferred).

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