Empowers Staffing Inc. - AI Data Engineer (ML Data Pipelines)

Client: Empowers Staffing Inc.

Contract: Freelance

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

• 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).

Additional Information

This role requires collaboration with multiple teams and a strong understanding of machine learning workflows and data needs. The position emphasizes the importance of efficient, high-quality data management within cloud environments.