AI DATA ENGINEER

Client: Altimetrik / S&P Global • Location: Princeton, NJ & NYC, NY • Employment: Full‑Time Hybrid • Experience: 15+ years • Work Authorization: Independent Visa required

Responsibilities

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

Qualifications

• 15+ years of overall professional experience.
• Bachelor s or Master s degree in Computer Science, Data Science, Machine Learning, or related field.
• Strong hands-on experience in data engineering and AI/ML workloads.
• Expert proficiency in Python.
• Strong experience with Databricks, Apache Spark, and distributed computing.
• Experience with ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Strong understanding of the machine learning lifecycle including training, deployment, and monitoring.
• Experience with feature engineering, data preprocessing, and ML data pipelines.
• Knowledge of vector databases, embeddings, and similarity search.
• Strong SQL skills.
• Understanding of data governance, model governance, and AI security.