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Lead AI Aplication Engineer (Infrastructure & LLMOps)

TechBiz Globalβ€’πŸŒ Remote Worldwideβ€’Estimated: $80,000 - $120,000

✨ AI Insights & Summary

TechBiz Global is seeking a Lead AI Application Engineer to join one of their client's innovative teams. This role offers a significant opportunity to architect and manage a shared AI platform, enabling cutting-edge AI services like Inference, Embeddings, and RAG as-a-service. With a focus on building robust infrastructure, curating AI services, managing data, and empowering developer self-service, this position is ideal for experienced platform engineers passionate about AI and MLOps, looking to shape the future of AI application development.

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.

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Job Overview

Posted6/19/2026
CategoryAI & Machine Learning
SourceJobsCollider

FAQ

Is this position remote?

The Lead AI Aplication Engineer (Infrastructure & LLMOps) role is a hybrid opportunity. The location specified is Remote Worldwide.

What is the salary?

The salary is not explicitly stated, but is competitive and based on experience.

How do I apply?

You can apply by clicking the "Apply for this role" button above to submit your application on the hiring website.

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