MLOps Engineer

About The Role The role owns the infrastructure, automation, and deployment pipelines that take machine learning models from local experiments to highly reliable, low-latency production services. The engineering team partners closely with data scientists and machine learning engineers to build robust platforms for model training, monitoring, and scaling. Key Responsibilities Design, build, and maintain end-to-end MLOps pipelines using Kubernetes, Docker, and Terraform on major cloud platforms Implement automated model training, validation, and deployment workflows using tools like Kubeflow, MLflow, or Argo Workflows Optimize model inference infrastructure for throughput and latency using GPU acceleration, ONNX, and TensorRT Deploy comprehensive monitoring and observability frameworks to track data drift, concept drift, and system resource utilization Establish security, compliance, and governance standards for datasets, model artifacts, and feature stores in production Collaborate with engineering teams to conduct code reviews, define architecture standards, and scale distributed training jobs What We Are Looking For 3–6 years of experience in MLOps, DevOps, or platform engineering, with a strong focus on machine learning infrastructure Deep hands-on experience with containerization, Kubernetes orchestration, and Infrastructure as Code (Terraform, Ansible) Proficiency in Python and Bash scripting for automation and tooling Familiarity with ML frameworks (PyTorch, TensorFlow) and model serving tools (Triton Inference Server, TorchServe, vLLM) Solid understanding of CI/CD pipelines, GitOps workflows, and cloud networking principles Bonus: Experience managing LLM infrastructure, vector databases, and distributed training clusters at scale