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Home / Lead AI Application Engineer (Infrastructure & LLMOps)

Lead AI Application Engineer (Infrastructure & LLMOps)

  • Full Time
  • Anywhere

TechBiz Global

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.

Originally posted on Himalayas

To apply for this job please visit himalayas.app.

Working in Canada

Canada is a country in North America. Its ten provinces and three territories extend from the Atlantic Ocean to the Pacific Ocean and northward into the Arctic Ocean, making it the second-largest country by total area, with the longest coastline of any country. Its border with the United States is the longest international land border. The country is characterized by a wide range of both meteorologic and geological regions. With a population of over 41 million, it has widely varying population densities, with the majority residing in its urban areas and large areas being sparsely populated. It

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