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# AI Data Engineer

- **Company:** ARSquare Tech LLC
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $130,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Artificial Intelligence, Data Architecture, Data Cleansing, Data Distribution Service, Information Engineering, Data Governance, Data Infrastructure, Data Security, Distributed Systems, Graph Database, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Unstructured Data, Feature Engineering, Pytorch, Apache Spark, Scikit Learn, Information Technology, Data Lineage, Machine Learning Operations, Virtual Agents, Drift Detection, Model Registry, Software Version Control, Data Pipelines, Databricks
- **Published:** October 16, 2026
- **Apply:** https://www.careerjet.com/job/us74c97e2896d3659391931726f7169626/eaa

## About the Role

* 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.

## Description

* 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.