MLOps Engineer - GPU & LLM Infrastructure

Job highlights

• 6+ years in MLOps with expertise in GPU provisioning, Kubernetes, Terraform, and ML CI/CD
• Design and build scalable MLOps infrastructure, automate model deployment, manage GPU-enabled AI/ML workloads, and optimize production LLM serving

Job Summary

We are looking for an experienced MLOps Engineer to build and manage scalable infrastructure for AI/ML and Large Language Model (LLM) workloads from scratch. The ideal candidate will have strong expertise in ML deployment pipelines, GPU infrastructure, Kubernetes, automation, and production model serving.

Key Responsibilities

• Design and build end-to-end MLOps infrastructure for model training, deployment, monitoring, and inference.
• Build CI/CD pipelines for ML models and automate model deployment, validation, and rollback.
• Provision, configure, and optimize GPU-enabled infrastructure for AI/ML and LLM workloads.
• Deploy and scale LLM inference and model-serving solutions in production environments.
• Manage Kubernetes, Docker, infrastructure-as-code, and distributed computing environments.
• Implement monitoring, logging, observability, and automated remediation for production AI workloads.
• Optimize GPU/CPU utilization, inference latency, system performance, and infrastructure costs.
• Collaborate with AI/ML engineers to build reliable, scalable, and high-performance AI platforms.

Required Skills

• 6+ years of experience in MLOps, ML Platform Engineering, SRE, DevOps, or Infrastructure Engineering.
• Strong hands-on experience with GPU provisioning, GPU-enabled infrastructure, and AI/ML workloads.
• Experience deploying and operating ML models and LLMs in production.
• Strong knowledge of Kubernetes, Docker, and container orchestration.
• Experience with Terraform, Ansible, Pulumi, or similar infrastructure-as-code tools.
• Proficiency in Python or Go for automation and infrastructure tooling.
• Experience with ML CI/CD, model serving, monitoring, Prometheus, Grafana, or OpenTelemetry.
• Strong understanding of cloud infrastructure, Linux, networking, and distributed systems.

Good to Have

• Experience with Ray, Slurm, vLLM, TensorRT-LLM, or Triton Inference Server.
• Exposure to GPU clusters, distributed training, NVIDIA GPU technologies, NVLink, or InfiniBand.
• Experience building AI infrastructure or MLOps platforms from scratch.

Preferred Candidate: Someone who can independently design, build, deploy, and optimize production-grade AI/ML and LLM infrastructurenot just maintain existing pipelines.