AI Data Engineer (ML Data Pipelines)

Job Description This is a remote position. We are seeking an AI Data Engineer to design and build production-grade data pipelines that power machine learning systems. This role focuses on creating scalable ingestion, transformation, and feature engineering workflows that support model training, evaluation, and real-time inference. You will work closely with Data Scientists, Machine Learning Engineers, and Platform teams to ensure high-quality, reliable, and efficient data flows across cloud environments. The ideal candidate understands both traditional data engineering and the unique data needs of ML systems. Key Responsibilities: • Design and build scalable data pipelines for ML workflows • Develop feature engineering and data preparation processes • Implement batch and real-time data ingestion systems • Ensure data quality, validation, and monitoring • Collaborate with ML engineers to support model training and deployment • Integrate pipelines with orchestration tools (Airflow or similar) • Optimize pipeline performance and cloud cost efficiency • Maintain documentation and version control of data workflows Requirements Requirements • 4+ years of experience in Data Engineering • Strong Python and SQL skills • Experience building data pipelines for ML or analytics systems • Hands-on experience with Spark, Databricks, or similar distributed processing frameworks • Experience with orchestration tools (Airflow or similar) • Experience in AWS, Azure, or GCP environments • Familiarity with data quality validation and monitoring frameworks • Understanding of feature engineering and model data lifecycle Preferred Qualifications: • Experience with streaming systems (Kafka, Kinesis, Pub/Sub) • Experience supporting model deployment and MLOps workflows • Experience with feature stores or vector databases • Familiarity with ML frameworks (TensorFlow, PyTorch)