ML Ops Engineer

We are seeking a skilled MLOps Platform Engineer to build and maintain a robust platform for the entire Machine Learning (ML) lifecycle. This role involves automating ML model training, testing, deployment, and monitoring, integrating seamlessly with our existing infrastructure to accelerate ML innovation.

• CI/CD for ML: Develop and manage CI/CD pipelines for ML model code and infrastructure.
• Automated ML Training: Design and implement pipelines for repeatable model training, data processing, and hyperparameter optimization. Include scheduling, queuing, and cost monitoring. Support training on cloud and on-premise GPU resources.
• Model Monitoring: Establish tools and dashboards for continuous monitoring of deployed ML models.
• Platform Integration: Ensure the MLOps platform integrates with company workflows, data pipelines, and computing architecture.
• MLOps Best Practices: Promote and implement best practices across the ML lifecycle.

Skills:

• Strong experience MLOps, DevOps, or ML Engineering, focusing on ML infrastructure
• Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Experience with CI/CD tools and workflow orchestrators (e.g., Airflow).