Lead AI Application Engineer (Infrastructure & LLMOps)

About this role

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. 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.

3. 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. 4. 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.