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.
• 2. 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.

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