MLOps Engineer - Python

About the Role :

We are looking for an MLOps Engineer with hands-on experience in building, deploying, and managing machine learning workflows and production ML systems. The ideal candidate should have strong Python skills, experience with MLOps tools and practices, and a good understanding of cloud, containerization, CI/CD, and ML lifecycle management.

Responsibilities :

• Build and maintain ML workflows and MLOps pipelines across the machine learning lifecycle.
• Develop and maintain automation for model training, validation, deployment, monitoring, and retraining.
• Implement ML lifecycle management practices including experiment tracking, model versioning, and model registry.
• Work with tools such as MLflow, Kubeflow, and Airflow for workflow orchestration and ML pipeline management.
• Build and maintain CI/CD pipelines using Jenkins, GitHub Actions, GitLab CI, or Argo CD.
• Containerize ML applications and services using Docker and deploy them using Kubernetes.
• Deploy and manage ML workloads on AWS, Microsoft Azure, or Google Cloud Platform.
• Implement model monitoring, performance tracking, logging, and alerting for production ML systems.
• Develop and integrate REST APIs for ML services using FastAPI or Flask.
• Troubleshoot production ML infrastructure, deployments, and pipeline failures.
• Work closely with Data Scientists, Data Engineers, Software Engineers, and DevOps teams to operationalize ML models.
• Follow best practices for scalability, reliability, security, and reproducibility of ML systems.

Required Skills :

• 2 - 7 years of experience in MLOps, ML Engineering, DevOps for ML, or a closely related role.
• Strong hands-on programming experience in Python.
• Strong understanding of Machine Learning workflows and the ML lifecycle.
• Hands-on experience with MLOps pipelines and production model deployment.
• Experience with MLflow, Kubeflow, or Airflow.
• Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Argo CD.
• Strong hands-on experience with Docker and Kubernetes.
• Experience with at least one major cloud platform : AWS, Azure, or GCP.
• Experience with model deployment, monitoring, versioning, experiment tracking, and model registry.
• Experience developing REST APIs using FastAPI or Flask.
• Good understanding of Linux and scripting.
• Strong troubleshooting and problem-solving skills.