MLOps / LLM Infra Engineers

Job Description – MLOps / LLM Infrastructure Engineer

Experience: 7–12 years

Role Overview

Responsible for hosting, deploying, and operating open-weight LLMs within a sovereign cloud environment. The role focuses on GPU infrastructure, model serving, performance optimization, and reliable model lifecycle management.

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 .