Lead AI Application Engineer – Infrastructure, LLMOps

Role Overview

• Build & Run the Shared AI Platform
• Architect and maintain a multi-tenant AI Platform for ML lifecycle
• Ensure high availability, low latency, and cost-efficiency
• Implement LLMOps/MLOps best practices
• Develop and expose

Requirements

• 8+ years in Platform Engineering, DevOps, or SRE
• 2+ years building AI/ML infrastructure or platforms
• Deep experience with Kubernetes (K8s), Docker, and Terraform/Pulumi
• Proven experience managing workloads across AWS/Azure/GCP and On-Premises
• Hands-on experience with vLLM, TGI (Text Generation Inference), or NVIDIA Triton
• Expertise in Vector DBs and traditional SQL/NoSQL databases
• High proficiency in Python and Go or Rust for platform tooling

Tech Stack

• AWS
• Azure
• Docker
• Google Cloud Platform
• Kubernetes
• NoSQL
• Python
• Rust
• SQL
• Terraform
• Go

Benefits

• Professional development opportunities
• Flexible working hours