Senior AI Data Engineer/ Architect

Altimetrik; New York (NY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Altimetrik is seeking an AI Data Engineer to design and optimize machine learning data pipelines, focusing on model tracking, lifecycle management, and governance integration. This role blends data engineering with ML expertise to support our data and AI initiatives.

You will build Databricks-based pipelines, implement feature engineering, model versioning, and real-time monitoring, collaborating with data scientists and ML engineers across our enterprise.

Qualifications

• Bachelor's or Master's degree in CS, data science, ML, or related field.
• 10-15 years in data engineering; at least 2 years on AI/ML workloads.
• Proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit-learn).
• Strong experience with Databricks, Spark, distributed ML workflows.
• Understanding of ML lifecycle, training, deployment, monitoring.
• Knowledge of vector databases, embeddings, and similarity search.
• Proficiency in SQL and data governance / AI ethics principles.

Responsibilities

• Design and implement data pipelines for AI model metadata, training data lineage, and metrics.
• Build data infrastructure on Databricks leveraging Spark for large-scale processing.
• Develop MCP servers and enable AI data distribution via MCP.
• Develop feature engineering pipelines and data preprocessing workflows.
• Implement model versioning, experiment tracking, and model registry integration with 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 data.
• Collaborate with data scientists to optimize data access patterns and feature stores.
• Implement security and compliance controls for sensitive AI data and artifacts.
• Create documentation for AI data architectures, schemas, and integration patterns.

Skills

Python

ML Frameworks

Databricks

Apache Spark

Model governance

Data pipelines

Feature engineering

SQL

Data quality

Collaboration

AI ethics

Education

Bachelor's or Master's in CS/Data Science/ML

Tools

Databricks

MLflow

Apache Spark

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.

About the Role

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

Client- Altimetrik

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.
• 10-15 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.