The MLOps Engineer bridges the gap between data science and production operations, ensuring seamless deployment, monitoring, and scalability of machine learning models. This role is critical to operationalizing AI solutions and maintaining model performance in real-world environments.
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Key Responsibilities:
- Collaborate with data scientists to package and deploy ML models into production environments.
- Design and maintain CI/CD pipelines for ML workflows.
- Automate model training, validation, and deployment processes.
- Monitor model performance and implement retraining strategies.
- Manage versioning of models, datasets, and code using tools like MLflow or DVC.
- Ensure compliance with data governance and security protocols.
- Optimize infrastructure for scalability and cost-efficiency (e.g., Kubernetes, Docker, cloud platforms).
- Troubleshoot production issues and implement robust logging and alerting systems.
Required Skills & Qualifications:
- Strong programming skills in Python; familiarity with Scala or Java is a plus.
- Experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization tools (Docker, Kubernetes).
- Hands-on experience with CI/CD tools (Jenkins, GitHub Actions, etc.).
- Knowledge of data pipeline tools (Apache Kafka, Airflow).
- Understanding of model lifecycle management and monitoring.
- Bachelor's or Master’s degree in Computer Science, Engineering, or a related field.
Location: Riyadh, Saudi Arabia
Work Conditions: On-site, Full-time
