AI Data Engineer

Altimetrik; New Jersey; On-site; USD 120,000 - 160,000; Full time

Job summary

Altimetrik is seeking an AI Data Engineer to build and optimize ML data pipelines, focusing on model tracking, lifecycle management, and governance integrations. You will work with data scientists to enable scalable AI infrastructure and ensure data quality across the enterprise.

The role emphasizes hands-on data engineering with deep ML knowledge, feature stores, and real-time monitoring. Collaboration across teams and strong security controls are essential.

Qualifications

• Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or related field.
• 9 years of hands-on data engineering experience, with at least 2 years on AI/ML workloads.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Strong experience with Databricks, Apache Spark, and distributed computing for ML workflows.
• Understanding of ML lifecycle including training, deployment, and monitoring.

Responsibilities

• Design and implement 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.
• Create feature engineering pipelines and data preprocessing workflows for model training and inference.
• Implement model versioning, experiment tracking, and model registry integration using MLflow.
• Create automated workflows for AI agent discovery, classification, and inventory management.
• Design and maintain knowledge graph structures for AI model relationships and data lineage.
• Build real-time data pipelines for 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 stores.
• Implement security and compliance controls for sensitive AI data and artifacts.

Skills

Python

ML frameworks

Databricks

Apache Spark

SQL

AI governance

Data governance

Model monitoring

Collaboration

Education

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

Tools

Databricks

Apache Spark

MLflow

PyTorch

TensorFlow

scikit-learn

Job description

Altimetrik delivers outcomes for our clients by rapidly enabling digital business & culture and infuse speed and agility into enterprise technology and connected solutions. We are practitioners of end-to-end business and technology transformation. We tap into an organization’s technology, people, and assets to fuel fast, meaningful results for global enterprise customers across financial services, payments, retail, automotive, healthcare, manufacturing, and other industries. Founded in 2012 and with offices across the globe, Altimetrik makes industries, leaders and Fortune 500 companies more agile, empowered and successful.

Altimetrik helps get companies get “unstuck”. We’re a technology company that lives organizations a process and context to solve problems in unconventional ways. We’re a catalyst for organization’s talent and technology, helping teams push boundaries and challenge traditional approaches. We make delivery more bold, efficient, collaborative and even more enjoyable.

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.
• 9 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.