Lead AI Aplication Engineer (Infrastructure & LLMOps)

About Us

TechBiz Global provides recruitment services for top clients. We are currently looking for a dedicated Lead AI Application Engineer to join one of our clients' teams.

Key Responsibilities

1. Build & Run the Shared AI Platform

• Architect and maintain a multi-tenant AI Platform supporting the full ML lifecycle across cloud and on-premises environments.
• Ensure high availability, low latency, and cost-efficiency for 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 squad interactions 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 for real-time AI applications and agentic workflows.
• Oversee Model Hosting environments using Kubernetes (K8s) and GPU orchestration.

4. Enable Developer Self-Service

• Build and maintain a Self-Service Portal or CLI for product squads to provision AI environments, models, and data stores.
• Reduce "Time-to-Inference" by providing pre-configured templates and blueprints.
• Conduct internal workshops and provide documentation to empower squads.

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 significant plus.