Lead AI Application Engineer (Infrastructure & LLMOps)

At TechBiz Global, we are providing recruitment service to our TOP clients from our portfolio.

We are currently looking for a dedicated Lead AI Aplication Engineer to join one of our clients' teams . If you're looking for an exciting opportunity to grow in an innovative environment, this could be the perfect fit for you.

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.

1. 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.

1. 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.

1. 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.