MLOps / LLM Infra Engineers

Experience: 7 12 years

Key Responsibilities:

• Deploy and operate models such as GPT, LLaMA, Gemma, Mistral, and other product/open-weight models within sovereign cloud.
  
• Manage GPU provisioning, capacity planning, utilization, and performance optimization .
  
• Implement LLM inference serving using platforms such as vLLM, Triton, or similar frameworks .
  
• Build model deployment, versioning, rollback, and lifecycle management processes.
  
• Develop MLOps pipelines for model packaging, testing, deployment, and monitoring.
  
• Monitor latency, throughput, GPU utilization, availability, and inference costs .
  
• Implement scalable and highly available model-serving infrastructure using Kubernetes and containers .
  
• Work closely with platform, security, and gateway teams to ensure secure model access and governance.
  
• Troubleshoot production issues across GPU, inference, Kubernetes, networking, and model-serving layers .
  

Required Skills:

• Strong experience in MLOps / LLM infrastructure / model serving .
  
• Hands - on experience with GPU-based inference and Kubernetes .
  
• Experience with vLLM, NVIDIA Triton, TensorRT-LLM, or equivalent.
  
• Experience deploying and managing LLaMA, Gemma, Mistral, GPT or similar LLMs .
  
• Knowledge of Docker, Kubernetes, CI/CD, model registries, and observability .
  
• Understanding of LLM quantization, batching, caching, GPU memory management, and inference optimization .
  
• Experience operating ML/LLM workloads in private, on-premises, or sovereign-cloud environments .
  

Preferred: Experience with NVIDIA GPUs, CUDA, Helm, Prometheus/Grafana, MLflow, and automated model deployment pipelines .