DATA & AI - Ops - Engineer

Requirements:

• Strong programming skills in Python, PySpark, SQL.
• Expertise in ML frameworks: scikit-learn, TensorFlow, PyTorch.
• Experience with model serialization formats (pickle, ONNX, TorchScript).

Job Description:

• Deploy models into production using Docker, Azure ML, AWS SageMaker, and Vertex AI with scalable serving frameworks.
• Develop CI/CD pipelines for ML workflows using GitHub Actions, MLflow CI/CD integrations, and container registries.
• Implement continuous training (CT), continuous integration (CI), and continuous delivery (CD) practices for ML systems.
• Automate data ingestion, preprocessing, and feature pipelines with PySpark and SQL.
• Monitor model performance, drift, and data quality in production environments.
• Implement logging, alerting, and observability for ML models and pipelines.

Skills Required:

• Strong programming skills in Python, PySpark, SQL.
• Familiarity with Kubernetes for scaling ML workloads.
• Experience with feature stores and monitoring tools (Feast, WhyLabs, Evidently AI, Prometheus, Grafana).
• Knowledge of data governance and compliance (GDPR, HIPAA, etc.).
• Exposure to large-scale distributed systems and real-time inference.