AI Platforms Leader Enterprise AI Platforms

Qualcomm Incorporated is seeking an experienced AI Platforms Leader to own the strategy, architecture, and operation of their end-to-end AI Platform. The role involves leading a high-caliber engineering team to deliver reliable, secure, and cost-efficient infrastructure for AI applications, while managing GPU clusters and cloud services.

Responsibilities:

• Own the AI Platform strategy & roadmap
• Define the multi‑year vision for a multi‑tenant, hybrid (on‑prem + cloud) AI platform, aligned to business needs, developer productivity, and cost efficiency
• Establish clear platform SLAs/SLOs, reliability goals, and security/compliance guardrails
• Run GPU-based compute at scale
• Operate and optimize on‑prem GPU clusters (e.g., Kubernetes + GPU operator and/or Slurm), including capacity planning, scheduling, partitioning, NCCL, and high‑throughput storage/networking
• Drive GPU utilization efficiency, right‑sizing, and cost transparency across training and inference workloads
• Deliver MLOps & LLMOps as a product
• Provide golden paths for data prep, training/fine‑tuning, model registry, lineage, governance, evaluation, red‑teaming, and safe deployment (batch, online, streaming)
• Implement CI/CD for models, prompts, and agents; automate evaluations and rollout/rollback with canaries, A/B, and shadow deployments
• Agentic AI, A2A, and MCP ecosystem
• Lead the design and operation of agentic orchestration (A2A patterns), tool integration, and MCP (Model Context Protocol) servers to securely expose enterprise tools and data
• Standardize agent capability schemas, guardrails, observability, and policy enforcement
• Cloud AI/ML platforms
• Leverage AWS/Azure AI services for training and inference (e.g., Bedrock/SageMaker/EKS; Azure AI Studio/Azure ML/AKS/Azure OpenAI) with robust networking, identity, secrets, and cost controls
• Establish multi‑cloud patterns for portability, resilience, and vendor risk management
• Platform engineering & DevOps excellence
• Own core platform services: identity/RBAC, secrets, service meshes, observability (logs/metrics/traces), data access controls, vector stores, feature stores, and model gateways (e.g., KServe/Triton/vLLM)
• Use GitOps/IaC (Terraform/Bicep/Helm) and secure software supply chain practices (SBOMs, image signing, policy as code)
• Operational leadership
• Lead a ~10‑engineer global team (platform, SRE, MLOps/LLMOps) with global collaboration, 24×7 readiness, and a healthy on‑call rotation
• Drive incident response, post‑mortems, and continuous improvement. Partner with Security, Legal, and Compliance for model/data governance
• Stakeholder & vendor management
• Partner with product, data, and application teams to enable high‑impact AI use cases
• Manage strategic vendors (e.g., cloud, GPU, enterprise AI tooling) and negotiate licenses/SOWs aligned to roadmap and budget