AI Data Engineer​/LaVista, NE​/Atlanta, GA

We are actively seeking an experienced, high-caliber, and technical AI Data Engineer with progressive professional experience in machine learning data pipelines, artificial intelligence infrastructure, and advanced data engineering to join our technology team on an urgent C2C contract basis with local onsite presence required in Scottsdale, AZ, LaVista, NE, or Atlanta, GA. In this critical AI and data engineering role, you will be responsible for building, scaling, and managing robust data pipelines, feature stores, and ingestion frameworks that power machine learning models and artificial intelligence applications.

You will leverage your deep specialist expertise in Python, cloud data platforms, MLOps tooling, and large-scale data processing to deliver uncompromised data quality, model readiness, and operational excellence. This opportunity offers competitive compensation, executive technical visibility, and exceptional career growth prospects within a premier technology consulting enterprise.

As an AI Data Engineer supporting our clients on-site in Scottsdale (AZ), LaVista (NE), or Atlanta (GA) on a Corporation-to-Corporation (C2C) contract basis, you will take full operational, tactical, and technical ownership of AI data pipeline development, feature engineering, model data preparation, and MLOps integration. Your day-to-day responsibilities include designing scalable data ingestion frameworks for unstructured and structured data, building and maintaining feature stores, collaborating with data scientists and machine learning engineers to operationalize AI models, optimizing data processing workflows using modern distributed computing frameworks, and ensuring stringent data security and governance compliance.

You will utilize your proactive troubleshooting and analytical problem-solving skills to resolve complex data architecture bottlenecks under strict project milestones. Furthermore, you will manage application requirements by sending your updated resume via direct email to Kayalvizhi Kannan at , ensuring strict compliance with local on-site presence requirements.

• Design, build, and maintain scalable data pipelines and ingestion frameworks specifically tailored for AI and machine learning workloads.
• Develop and manage feature stores, data cleansing routines, and preprocessing workflows to support high-performance ML models.
• Collaborate closely with data scientists and AI researchers to translate research models into robust production-grade data services.
• Implement MLOps best practices for automated data validation, model retraining triggers, and pipeline monitoring.
• Utilize distributed data processing frameworks (Spark, Ray, Dask) to handle massive datasets efficiently across cloud environments.
• Ensure strict data quality, lineage tracking, and governance compliance across all artificial intelligence data stores.
• Troubleshoot and resolve data ingestion bottlenecks, pipeline failures, and latency issues in production AI systems.
• Author meticulous technical documentation, data architecture schemas, MLOps runbooks, and pipeline integration guides.

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Engineering, or a related technical discipline.
• Minimum of 5+ years of progressive professional experience in software engineering, data engineering, and AI/ML data pipelines.
• Strong, demonstrable hands-on coding proficiency in Python and SQL for data manipulation and pipeline development.
• Deep practical experience with cloud data platforms (AWS, Azure, or GCP) and distributed data processing (Apache Spark).
• Solid practical understanding of MLOps principles, feature engineering, and model deployment data requirements.
• Exceptional analytical, troubleshooting, debugging, and problem-solving capabilities under high-pressure enterprise environments.
• Excellent written and verbal English communication skills with strong cross-functional collaboration abilities.
• Mandatory local availability and willingness to work on-site in Scottsdale, AZ, LaVista, NE, or Atlanta, GA (C2C contract engagement).

• Professional certifications in cloud engineering or machine learning data engineering.
• Prior professional working experience in AI-driven startups, enterprise data science labs, or technology consulting firms.
• Hands-on familiarity with vector databases (Pinecone, Milvus, Qdrant) and LLM data ingestion frameworks (Lang Chain, Llama Index).
• Experience with containerization and…