MLOps Engineer

About the role

The role owns the infrastructure, automation, and scaling of machine learning workflows, ensuring that models transition seamlessly from research prototypes to highly reliable production services. You will bridge the gap between data science and software engineering by building robust CI/CD pipelines, optimizing inference latency, and establishing enterprise-grade MLOps standards.

Responsibilities

• Design, build, and maintain scalable MLOps pipelines for automated model training, validation, packaging, and deployment
• Implement containerized model serving architectures using Docker, Kubernetes, and Triton Inference Server or FastAPI
• Set up comprehensive monitoring systems to track model performance, data drift, concept drift, and system resource utilization
• Manage feature stores and automated data pipelines to ensure consistency between training features and real-time inference
• Collaborate with machine learning engineers and data scientists to optimize model latency, throughput, and cloud infrastructure costs
• Enforce security, governance, and reproducibility standards across all machine learning artifacts and experiments

Requirements

• 3–6 years of experience in MLOps, DevOps, or machine learning engineering with a strong focus on production infrastructure
• Deep expertise with container orchestration tools like Kubernetes and cloud platforms such as AWS, GCP, or Azure
• Hands-on experience with ML lifecycle and orchestration tools: MLflow, Kubeflow, Airflow, or Metaflow
• Strong proficiency in Python, Infrastructure as Code (Terraform/CloudFormation), and CI/CD pipelines (GitHub Actions, GitLab CI)
• Solid understanding of networking, security, and performance tuning for high-throughput, low-latency machine learning services
• Bonus: Experience deploying and scaling Large Language Models (LLMs) or GenAI architectures in production