AI Data Engineer

[email protected] • Princeton, NJ

1d • Hybrid

About The Position

The AI Data Engineer designs and implements data infrastructure and pipelines for AI/ML workloads. The role builds large-scale distributed processing solutions on Databricks and Spark, supports AI data distribution via MCP, and develops workflows for feature engineering, model tracking, AI agent inventory, monitoring, and data quality. The engineer also maintains knowledge graph structures, integrates model registries and experiment tracking, applies security and compliance controls, and documents AI data architectures and integration patterns. The position is full-time and hybrid in Princeton, New Jersey, and New York City, with a stated salary of $130,000 USD.

Requirements

• Bachelor's or master's degree in Computer Science, Data Science, Machine Learning, or a related field.
• 5–7 years of hands-on data engineering experience, including at least 2 years focused on AI/ML workloads.
• Expert proficiency in Python and experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Strong experience with Databricks, Apache Spark, and distributed computing for ML workflows.
• Deep understanding of the machine learning lifecycle, including model training, deployment, and monitoring.
• Experience with feature engineering, data preprocessing techniques, and ML data pipelines.
• Knowledge of vector databases, embeddings, and similarity search for AI applications.
• Proficiency in SQL for structured and unstructured data management.
• Understanding of data governance, model governance, and AI ethics principles.
• Strong analytical and problem-solving capabilities with attention to data quality.
• Excellent collaboration skills for working with data scientists, ML engineers, and architects.

Responsibilities

• Design and implement specialized data pipelines for AI model metadata, training data lineage, and model performance metrics tracking.
• Build data infrastructure on Databricks using Spark for large-scale distributed dataset processing.
• Develop MCP servers and enable AI data distribution via MCP.
• Develop feature engineering pipelines and data preprocessing workflows for AI model training and inference.
• Implement model versioning, experiment tracking, and model registry integration using MLflow or similar tools.
• Create automated workflows for enterprise AI agent discovery, classification, and inventory management.
• Design and maintain knowledge graph structures representing AI model relationships, dependencies, and data lineage.
• Build real-time data pipelines for AI model monitoring, drift detection, and performance tracking.
• Develop data quality frameworks for AI training and validation datasets.
• Collaborate with data scientists to optimize data access patterns and feature store implementations.
• Implement security and compliance controls for sensitive AI training data and model artifacts.
• Create documentation for AI data architectures, schemas, and integration patterns.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level