Leader, Enterprise AI Platforms

Qualcomm

Confirmed live yesterday High trust
Closes tomorrow

Quick summary

Work type
On-site
Location
San Diego, CA
Salary
$198,500–$297,700 / yr
Posted
179 days ago
Freshness
Confirmed live yesterday
Closes
Sep 12, 2026 (soon)

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $208k
This role $248k
$152k most similar roles pay here $313k

This role pays more than 75% of similar roles. Most pay $167,975–$247,300 — the shaded band above. At the midpoint, this role pays about $248k versus about $208k for comparable roles.

Based on 239 similar postings.

Employer

About Qualcomm

Qualcomm is a leading American semiconductor and telecommunications company based in San Diego, CA.

Qualcomm currently has 623 open roles on FindRole.

Listed pay typically runs $148,300–$222,500 across 603 roles with salary data.

Most-posted roles

View all roles at Qualcomm

At a glance

TL;DR · Leader, Enterprise AI Platforms

AI Platforms Leader Enterprise AI Platforms leads a high-caliber engineering team to oversee the strategy, architecture, and operation of an end-to-end AI Platform. This role involves managing multi-tenant, hybrid infrastructure spanning on-prem GPU clusters and cloud services across AWS, GCP, and Azure. The leader will build robust, self-service capabilities for training, fine-tuning, inference, and agentic orchestration, including A2A patterns and MCP servers. Key responsibilities include optimizing GPU utilization via Kubernetes or Slurm, implementing MLOps and LLMOps pipelines, and managing core services like vector stores, feature stores, and model gateways. Technical requirements include proficiency in Python, C++, and C, alongside experience with PyTorch, CUDA, Triton, vLLM, and KServe. The role solves the challenge of providing secure, cost-efficient infrastructure for internal builders while ensuring high availability through GitOps, IaC tools like Terraform, and comprehensive security guardrails.

What you'll do

  • Define the multi-year strategy, roadmap, and infrastructure requirements for a hybrid on-prem and cloud AI platform.
  • Manage and optimize large-scale GPU clusters using Kubernetes or Slurm to ensure high utilization and cost efficiency.
  • Deliver MLOps and LLMOps capabilities including data preparation, model registry, automated evaluation, and safe deployment pipelines.
  • Design and operate agentic orchestration systems, including A2A patterns and Model Context Protocol (MCP) servers.
  • Manage multi-cloud AI services across AWS, Azure, and GCP while maintaining security, identity, and cost controls.
  • Oversee core platform engineering tasks such as infrastructure as code, service meshes, vector stores, and model gateways.
  • Lead a global team of engineers to manage 24/7 operations, incident response, and continuous improvement.
  • Manage strategic vendor relationships and negotiate contracts for cloud services, hardware, and enterprise AI tools.

What we're looking for

  • Bachelor's degree in Engineering, Computer Science, or a related field is required.
  • 15+ years of overall engineering experience including 10 years building and operating large-scale platforms.
  • 5+ years of experience leading a team of approximately 10 engineers across platform, SRE, MLOps, and LLMOps.
  • Hands-on expertise in operating on-prem GPU clusters using Kubernetes with GPU operators or Slurm.
  • Extensive experience with MLOps/LLMOps including model lifecycle management, evaluation, and safety guardrails.
  • Deep experience with cloud AI/ML services and managed Kubernetes across AWS, GCP, and Azure.
  • Proficiency in DevOps practices including CI/CD, GitOps, Infrastructure as Code (Terraform/Bicep/Helm), and containerization.
  • Solid understanding of agentic AI orchestration, A2A patterns, and Model Context Protocol (MCP) servers.

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