Duties and Responsibilities:
- Build and maintain automated machine learning workflows, including data preprocessing, training, evaluation, and deployment.
- Collaborate with data scientists, ML engineers, and DevOps teams to integrate automation into production pipelines.
- Develop CI/CD systems for ML models to streamline updates and reduce operational overhead.
- Monitor and log ML model performance to ensure robustness, reproducibility, and compliance.
- Identify bottlenecks in ML workflows and propose solutions for automation and scalability.
- Ensure alignment with best practices in MLOps, model governance, and infrastructure management.
- Create reusable components and templates for model development across projects.
- Stay current with developments in ML tools, cloud automation, and orchestration frameworks.
Requirements and Qualifications:
- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
- Professional experience in machine learning engineering, DevOps, or MLOps.
- Strong programming skills in Python and familiarity with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Experience with workflow orchestration tools (e.g., Airflow, Kubeflow, MLflow).
- Knowledge of containerization (Docker), cloud platforms (AWS, GCP, Azure), and CI/CD practices.
- Ability to balance technical implementation with operational efficiency and reliability.
