AI Data Engineer (ML Data Pipelines)

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

• 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)