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 workplace. 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.

📌 MLOps / LLM Infra Engineers (Mumbai)
🏢 algoleap
📍 Mumbai