AI Data Engineer

AIT Global inc.; Princeton (NJ); Hybrid; USD 120,000 - 180,000; Full time

Job summary

AIT Global inc. in Princeton/NYC is seeking an AI Data Engineer to design and optimize machine learning data pipelines, focusing on model metadata, data lineage, and governance integration.

You will build Spark-based pipelines on Databricks, implement model versioning and monitoring, develop feature engineering workflows, and collaborate with data scientists to ensure scalable, compliant AI data architectures.

Qualifications

• Bachelor's or Master's degree in CS, DS, ML, or related field.
• 5-7 years of hands-on data engineering experience, with at least 2 years in AI/ML workloads.
• Proficiency in Python and ML frameworks (PyTorch/ TensorFlow/ scikit-learn).
• Strong experience with Databricks, Spark, and distributed ML data pipelines.
• Knowledge of data governance and AI ethics principles.

Responsibilities

• Design and optimize ML data pipelines for model metadata and data lineage.
• Build data infrastructure on Databricks with Spark for large-scale processing.
• Develop feature engineering pipelines and model/version tracking.
• Implement model governance, monitoring, and drift detection.
• Collaborate with data scientists to optimize feature stores and data access.

Skills

Team collaboration

Analytical thinking

Problem solving

Documentation

Security/compliance

Education

Bachelor's or Master's in CS/DS/ML

Tools

Python

Databricks

Apache Spark

MLflow

SQL

Neo4j

LangChain

HuggingFace

Azure ML

Job description

Job Title: AI Data Engineer
Location: Princeton, NJ & NYC, NY (Hybrid)

Role Overview:

The AI Data Engineer will specialize in building and optimizing machine learning data pipelines, focusing on AI model tracking, lifecycle management, and integration with AI governance systems. This role combines data engineering expertise with AI/ML knowledge to support the organization's broader data and AI infrastructure initiatives.

Key Responsibilities:

• Design and implement specialized data pipelines for AI model metadata, training data lineage, and model performance metrics tracking.
• Build data infrastructure on Databricks leveraging 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 AI agent discovery, classification, and inventory management across the enterprise.
• Design and maintain knowledge graph structures for 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 specific to AI training datasets and validation data.
• 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 comprehensive documentation for AI data architectures, schemas, and integration patterns.

Required Skills and Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.
• 5-7 years of hands-on experience in data engineering, with 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 processes.
• 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.

Preferred/Nice-to-Have Skills:

• Experience with generative AI applications, including RAG (Retrieval-Augmented Generation) and fine-tuning.
• Knowledge of LangChain, HuggingFace, or other GenAI frameworks.
• Familiarity with Azure ML, AWS SageMaker, or Google Vertex AI platforms.
• Experience with graph databases (Neo4j, Amazon Neptune) for knowledge graph implementation.
• Understanding of AI model explainability and interpretability techniques.
• Experience with A/B testing frameworks for ML model evaluation.
• Certification in Databricks, AWS, Azure, or GCP AI/ML services.
• Publications or contributions to open-source ML projects