Remote - Machine Learning Operations Engineer

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Requirements:

• Strong programming skills in Python.
• Experience with MLOps frameworks (e.g., MLflow, Kubeflow, TensorFlow Extended).
• Familiarity with cloud platforms (AWS, GCP, Azure).
• Experience with containerization tools (Docker, Kubernetes).
• Close familiarity with CI/CD tools (e.g., Jenkins, GitHub Actions, GitLab CI/CD).
• Expertise in data preprocessing, validation, and feature engineering.
• Knowledge in model versioning and reproducibility tools.
• Experience with monitoring frameworks for ML (e.g., Evidently, Prometheus, Grafana).
• Understanding of A/B testing, model drift, and performance tracking.

Responsibilities:

• Design, implement, and manage CI/CD pipelines for ML model deployment and monitoring.
• Automate model training, validation, and deployment processes.
• Maintain versioning for data, models, and training processes.
• Develop and manage infrastructure for scalable model training and deployment on cloud platforms.
• Act as an architect designing foundational building blocks for projects.
• Optimize resource utilization for training and inference workloads.
• Implement monitoring systems for models tracking performance metrics.
• Ensure model reliability with automated alerting systems.
• Define rollback strategies for underperforming models.
• Collaborate with teams to align ML pipelines with business objectives.
• Create detailed documentation for processes and workflows.
• Contribute to cross-functional knowledge sharing through training materials.
• Implement data validation and preprocessing pipelines.
• Maintain feature stores and model registries.
• Ensure compliance with industry regulations.
• Stay updated on MLOps trends and propose system improvements.
• Explore and integrate new tools to enhance productivity.