Lead AI Application Engineer – Infrastructure, LLMOps

Responsibilities

• Build & Run the Shared AI Platform
• Architect and maintain a multi‑tenant AI Platform that supports the full ML lifecycle across cloud and on‑premises environments
• Ensure high availability, low latency, and cost‑efficiency for all shared AI resources
• Implement LLMOps/MLOps best practices, including automated deployment pipelines for models
• Curate the AI Services Catalogue
• Develop and expose “as‑a‑service” capabilities: Inference‑as‑a‑Service, Embeddings‑as‑a‑Service, and RAG‑as‑a‑Service
• Standardize how squads interact with LLMs, providing unified APIs and abstraction layers to prevent vendor lock‑in
• Manage AI Data Infrastructure
• Own the deployment and scaling of Vector Databases (e.g., Pinecone, Milvus, Weaviate) and Feature Stores (e.g., Feast, Tecton, Hopsworks)
• Optimize data retrieval patterns to support real‑time AI applications and agentic workflows
• Oversee Model Hosting environments, utilizing Kubernetes (K8s) and GPU orchestration to manage compute resources efficiently
• Enable Developer Self‑Service
• Build and maintain a Self‑Service Portal or CLI that allows product squads to provision AI environments, models, and data stores independently
• Reduce “Time‑to‑Inference” for new features by providing pre‑configured templates and blueprints
• Conduct internal workshops and provide documentation to empower squads to use the platform effectively

Requirements

• Must‑Have Technical Skills
• Infrastructure: Deep experience with Kubernetes (K8s), Docker, and Terraform/Pulumi
• Hybrid Cloud: Proven experience managing workloads across AWS/Azure/GCP and On‑Premises (NVIDIA AI Enterprise, OpenShift)
• AI/ML Tooling: Hands‑on experience with vLLM, TGI (Text Generation Inference), or NVIDIA Triton for model serving
• Databases: Expertise in Vector DBs and traditional SQL/NoSQL databases
• Languages: High proficiency in Python and Go or Rust for platform tooling
• Experience 8+ years in Platform Engineering, DevOps, or Site Reliability Engineering (SRE)
• 2+ years specifically focused on building AI/ML infrastructure or platforms
• Experience building Internal Developer Platforms (IDP) is a massive plus