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.

Positive 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.

📌 MLOps Engineer - GPU & LLM Infrastructure (Gurugram)
🏢 Trine Infotech
📍 Gurugram