MLOps Engineer

About The Role The role owns the infrastructure and pipelines that support high-throughput, low-latency machine learning and LLM systems in production. The team works closely with machine learning engineers and data scientists to automate deployments, ensure reliable model serving, and maintain robust MLOps standards. Key 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 comprehensive monitoring systems 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 What We Are Looking For 3–6 years of experience in MLOps, DevOps, or machine learning engineering with a heavy focus on production infrastructure Strong proficiency in Python, Bash, and infrastructure orchestration tools such as Terraform and Kubernetes Hands-on experience with cloud ML platforms and managed services across 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