VP, AI Platform Engineer – Secure Research Cloud

JPMorganChase

New York (NY)

On-site

USD 180,000 - 260,000

Full time

6 days ago
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Job summary

JPMorgan Chase & Co. GTAR seeks an AI Platform Engineer – Vice President to lead the architecture and operation of the shared AI research platform in a secure, scalable, cloud-enabled environment in New York.

You will collaborate with researchers and enterprise tech teams to ensure reproducible experimentation and smooth downstream integration. You will oversee containerized workflows, IaC pipelines, GPU workloads, and performance/cost optimization while guiding reliability, security, and

Qualifications

  • Master's degree in a technical field with 4+ years in cloud/platform infra.
  • Experience operating production-grade cloud/platform environments.
  • Proficiency in Python, shell, Linux, containers, and IaC tooling.

Responsibilities

  • Own architecture, roadmap, and day-to-day engineering of the AI research platform.
  • Design and operate secure, scalable CPU/GPU infrastructure and clusters.
  • Build reproducible, self-service environments and automation.
  • Translate research requirements (LLM, retrieval, evaluation) into platform capabilities.
  • Onboard new AI infra tech, cloud services, and tooling with benchmarks and pilots.
  • Maintain observability, incident response, and lifecycle management.

Skills

Python
Shell scripting
Linux
Containers
Terraform
CloudFormation
Kubernetes
Jupyter
Ray/Slurm
AI/ML workflows

Education

Master's degree in computer science or related field

Tools

Terraform
CloudFormation
Kubernetes
EC2
GPU software stacks
CI/CD
Artifact repositories

Job description

JPMorgan Chase & Co. GTAR seeks an AI Platform Engineer – Vice President to lead the architecture and operation of the shared AI research platform in a secure, scalable, cloud-enabled environment in New York.

You will collaborate with researchers and enterprise tech teams to ensure reproducible experimentation and smooth downstream integration. You will oversee containerized workflows, IaC pipelines, GPU workloads, and performance/cost optimization while guiding reliability, security, and

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