Lead AI Application Engineer (Infrastructure & LLMOps)

Key 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.