MLOps / DevOps Engineer

About the Role

We are looking for an MLOps/DevOps Engineer to build, deploy, and operate infrastructure for LLM and AI workloads in production. You will work closely with ML and backend engineers to create reliable environments for training/fine\-tuning, model serving, and GPU\-based compute, ensuring performance, scalability, and high availability.

Key Responsibilities

• Design and manage scalable infrastructure for AI/ML workloads (training, fine\-tuning, inference).
• Deploy, manage, and optimize GPU\-enabled environments (drivers, CUDA runtime readiness, GPU monitoring, scheduling).
• Build and maintain CI/CD pipelines for backend services (APIs, microservices), and
• ML/LLM deployments (model versioning, rollout, rollback).
• Containerize and orchestrate services using Docker and Kubernetes (EKS/GKE/AKS or self\-managed).
• Implement best practices for MLOps lifecycle:
• model packaging and artifact management
• reproducible deployments
• environment management across dev/stage/prod
• Set up observability (metrics, logging, alerting, tracing) for infrastructure and model services.
• Improve system reliability via SRE practices: incident response, root\-cause analysis, SLAs/SLOs, capacity planning.
• Collaborate with ML engineers to productionize LLM workflows (LoRA adapters, inference endpoints, batch jobs).

Required Skills \& Qualifications (Must Have)

• 3–5 years experience in DevOps / Platform Engineering / MLOps role
• Strong Linux administration and networking fundamentals.
• Hands\-on experience with Docker and Kubernetes (deployments, services, ingress, scaling).
• Experience building CI/CD pipelines (GitHub Actions / GitLab CI / Jenkins).
• Proficiency in scripting/automation using Python (or strong bash \+ ability to work in Python).
• Cloud experience with AWS / GCP / Azure (compute, networking, IAM, storage).
• Familiarity with infrastructure automation and configuration management (Terraform/Ansible is a plus).

Good to Have (Preferred)

• Experience with model serving frameworks: vLLM, Triton Inference Server, TorchServe, Ray Serve.
• Exposure to ML lifecycle tools: MLflow, Weights \& Biases, DVC.
• Understanding of LLM fine\-tuning concepts (LoRA/QLoRA) and deployment requirements.
• Experience working with distributed systems, job schedulers, or workflow orchestration (Argo, Airflow, Prefect).
• Knowledge of vector databases / RAG pipelines (FAISS, Pinecone, Weaviate, pgvector).
• Familiarity with GPU performance tuning/monitoring (nvidia\-smi, DCGM, Prometheus exporters).

Experience

• LLM: 3 years (Required)
• Ai architecture: 3 years (Required)
• DevOps engineer: 3 years (Required)

Work Location: In person