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:

1. 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.

• Experience building Internal Developer Platforms (IDP) is a massive plus.