MLOps Engineer – Python
Job Description
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.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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