Lead AI Application Engineer (Infrastructure & LLMOps)

Brief intro

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.

Key details

• 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