AI Data Engineer

Lorven Technologies Inc.; Princeton (NJ); Hybrid; USD 130,000 - 200,000; Full time

Job summary

Lorven Technologies Inc. is seeking a senior data engineer to design and implement data pipelines for AI model metadata, training data lineage, and model performance tracking.

You will build data infrastructure on Databricks and Spark for large-scale processing and develop feature engineering pipelines for AI model training and inference. You will also implement model versioning, experiment tracking, and registry integration, while creating automated workflows for AI agent discovery and

Qualifications

• 5-7 years in data engineering with 2+ years in AI/ML workloads.
• Strong Python and ML frameworks experience; Databricks and Spark for ML workflows.

Responsibilities

• Design and implement data pipelines for AI model metadata and training data lineage.
• Build data infrastructure on Databricks and Spark for large-scale processing.
• Develop ML feature engineering pipelines and data preprocessing workflows.
• Implement model versioning, experiment tracking, and model registry integration.
• Create automated workflows for AI agent discovery, classification, and inventory management.
• Design knowledge graphs for AI model relationships and data lineage.
• Build real-time data pipelines for model monitoring and drift detection.
• Ensure data governance, security, and AI ethics in data handling.
• Collaborate with data scientists to optimize feature stores.
• Create documentation for AI data architectures and integration patterns.

Skills

Python

ML frameworks

Databricks

Apache Spark

SQL

Data engineering

Collaboration

Education

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

Tools

Databricks

Apache Spark

Job description

Location- Princeton, NJ & NYC, NY (Hybrid)

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 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.