MLOps Engineer

🔧 Key Responsibilities:

1. Design, develop, and maintain CI/CD pipelines for ML models.
2. Collaborate with Data Scientists to build scalable model deployment architectures.
3. Automate data and model validation, retraining, and monitoring pipelines.
4. Ensure reliability, security, and scalability of ML infrastructure.
5. Monitor model performance and data drift in production environments.
6. Integrate ML models with APIs, web services, and production systems.
7. Implement best practices for version control and reproducibility of ML workflows.
8. Manage containerized ML applications using Docker and Kubernetes.

🎓 Qualifications:

1. Bachelors or Master’s degree in Computer Science, Engineering, or a related field.
2. Proven experience in MLOps, DevOps, or ML Engineering roles.
3. Proficiency in Python, Bash, and scripting languages.
4. Hands-on experience with ML platforms (e.g., MLflow, Kubeflow, SageMaker).
5. Familiarity with cloud platforms (AWS, Azure, GCP).
6. Experience with CI/CD tools (e.g., Jenkins, GitHub Actions).
7. Strong understanding of ML model lifecycle and deployment challenges.
8. Excellent problem-solving and communication skills.

⭐ Preferred Skills:

1. Experience with infrastructure-as-code tools (e.g., Terraform).
2. Knowledge of data engineering and ETL pipelines.
3. Exposure to monitoring tools like Prometheus, Grafana.