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.