MLOps Engineer

Role Overview

MLOps Engineer owning the infrastructure and pipelines that enable high-throughput, low-latency ML/LLM systems in production. You will collaborate with ML engineers and data scientists to automate deployments, ensure reliable model serving, and uphold strong MLOps practices.

Responsibilities

• Build and maintain scalable CI/CD pipelines for automated model training, validation, and deployment.
• Deploy and manage containerized model serving endpoints using Kubernetes, Docker, and MLflow or BentoML.
• Implement monitoring for data drift, concept drift, system latency, and model degradation.
• Optimize cloud infrastructure costs and resource utilization across GPU clusters and distributed inference environments.
• Collaborate with engineering teams to enforce MLOps best practices, security standards, and automated testing.
• Design and provision cloud infrastructure using Terraform and Infrastructure as Code (IaC) principles.

Requirements

• 3–6 years of experience in MLOps, DevOps, or machine learning engineering focused on production infrastructure.
• Strong proficiency in Python and Bash, plus infrastructure orchestration tools such as Terraform and Kubernetes.
• Hands-on experience with cloud ML platforms and managed services on AWS, GCP, or Azure.
• Familiarity with modern model serving frameworks, feature stores, and vector database deployments.

Bonus

• Experience managing GPU clusters for large language model fine-tuning and inference optimization.