Qualcomm
AI Platforms Leader Enterprise AI Platforms
$203K–$304K
San Diego, California · On-site · Full-time · Posted Sep 17, 2026
- Experience
- 15+ years overall, including ~10 years building and operating large-scale platforms; 5+ years leading a team of ~10 engineers. Minimum qualifications also state: bachelor’s and 8+ years, or master’s and 7+ years, or PhD and 6+ years of software engineering or related work experience; 4+ years with a programming language.
- Education
- Bachelor’s degree in Engineering, Computer Science, or related field.
About the job
Leads the strategy, engineering, and operation of Qualcomm’s hybrid AI platform, enabling teams to train, deploy, and operate AI models and agents securely and reliably.
What you'll do
- Set the platform roadmap and reliability, security, and cost goals; operate on-prem GPU clusters and cloud AI services.
- Deliver self-service MLOps/LLMOps capabilities, including model lifecycle workflows, evaluation, deployment, and agent orchestration with A2A patterns and MCP servers.
- Lead a global engineering team, operational response, and partnerships with product, security, legal, compliance, and vendors.
What they ask for
- 15+ years of overall engineering/technology experience, including ~10 years building and operating large-scale platforms; proven team leadership for 5+ years.
- Hands-on operation of on-prem GPU clusters, including scheduling, capacity planning, performance tuning, and reliability.
- Experience with model lifecycle, evaluation, safety/guardrails, and observability; deep cloud AI/ML and managed Kubernetes experience.
- Experience with CI/CD, GitOps, IaC, Docker, Kubernetes, secure SDLC, agent orchestration, MCP servers, and distributed teams.
Nice to have
- Master’s or PhD in CS/EE/Math or related field.
- Experience with the listed training/inference, storage, MLOps, security/governance, and agentic frameworks.
Pay
$202,900.00 - $304,300.00 USD (pay range; salary)
Tools
AWS, GCP, Azure, Kubernetes, Slurm, Terraform, Bicep, Helm
AI focus
Leads enterprise AI platform work for training, fine-tuning, inference, retrieval, MLOps/LLMOps, and agentic orchestration, including A2A patterns and MCP servers.
Model-written summary · the original posting is authoritative