Senior AI Data Engineer

Data Architecture: Design and implement scalable data pipelines (batch + streaming) to ingest, transform, and serve data for AI use cases.

Feature Engineering: Build and maintain reusable feature stores ensuring consistency between training and inference.

Data Quality & Governance: Implement validation, lineage, and observability frameworks to ensure accuracy, reliability, and compliance.

Collaboration: Work with Data Scientists to prepare model training data and with AI Engineers to deliver real-time data flows for inference.

Infrastructure: Manage data storage, orchestration, and compute for ML pipelines (Spark, Airflow, etc).

Performance & Cost Optimization: Continuously tune data workflows for efficiency and scalability on cloud infrastructure.

Security & Compliance: Enforce privacy, encryption, access control, and retention policies for all AI data assets.

Automation: Contribute to CI/CD for data pipelines and participate in shared MLOps activities (e.g., automated retraining triggers).

Documentation: Maintain clear metadata, schema definitions, and data contracts to enable collaboration and traceability.