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.