AI Platforms Leader Enterprise AI Platforms Software Engineering Posted ago

** General

Summary:

** We’re seeking an experienced AI Platforms Leader to own the strategy, architecture, and operation of our end‐to‐end AI Platform—spanning on‐prem GPU clusters and cloud services (AWS/GCP/Azure). You’ll lead a high-caliber engineering team to deliver reliable, secure, and cost‐efficient infrastructure for training, fine‐tuning, inference, retrieval, and agentic orchestration (including A2A patterns and MCP servers). If you love turning complex AI/ML requirements into robust, self‐service platform capabilities for builders across the company, this is your role.
** This role requires full-time onsite work in San Diego, CA (5 days per week).
**** Key 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/Sage Maker/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 & Dev Ops 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 Git Ops/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.
** Required Qualifications**
* ** 15+ years
** overall engineering/technology experience, including **~10 years
** building and operating large‐scale platforms (AI/ML, data, or high‐performance computing).
* ** Leadership:
** Proven experience leading a team of ~10 engineers for 5+ years, across platform/SRE/MLOps/LLMOps, with coaching, hiring, performance management, and clear execution rhythms.
* ** GPU cluster expertise:
** Hands‐on operations for on‐prem GPU clusters (Kubernetes…