What you'll be doing

Designing and maintaining data pipelines optimized for ML/AI workloads , including handling of large-scale, unstructured, and semi-structured data. Building feature pipelines and feature stores that ensure reusability and consistency of data used by machine learning models. Collaborating with Data Scientists and ML Engineers to understand data requirements for training, validation, and production deployment . Ensuring data quality, lineage, and governance meet standards required for AI/ML applications. Supporting MLOps practices by integrating data pipelines with model training, monitoring, and deployment workflows. Leveraging distributed processing frameworks (e.g., Spark, Databricks, Azure Synapse) for scalable ML data processing .

Desired Candidate Profile

• 5+ years of experience as a Data Engineer, working with Azure and Databricks, ideally with exposure to ML/AI-related data workflows .
• College degree that demonstrates your analytic abilities, such as Econometrics, Computer Sciences, Mathematics or similar;
• Excellent analytical and problem-solving skills;
• Experience with data preparation for ML/AI : managing large datasets, feature engineering, and real-time or batch data pipelines.
• Familiarity with MLOps concepts and how data engineering supports model lifecycle management.
• Experience with orchestration frameworks (Airflow, Prefect, or Azure Data Factory) for complex ML pipelines .
• Knowledge of unstructured data processing (text, images, logs) is a plus.
• Strong SQL and Python skills; experience with distributed data processing (PySpark, Dask, etc.) is a plus.