Remote AI Data Engineer: ML Pipelines & Feature Engineering

• Work Experience Python, SQL, Spark, Databricks, Airflow, Feature Engineering, Data Pipelines, Data Quality, Great Expectations, AWS, Azure, GCP, Kafka
• Required Skills
• Airflow
• AWS
• +20
• Remote Job

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)