Senior AI/ML Data Platform Engineer

Rate: $60-65/hr C2C

Position Overview:

The Senior AI/ML Data Platform Engineer will lead the architecture, development, and continuous improvement of shared enterprise data products and AI-enabled data services. The role focuses on scalable data platforms, real-time streaming, data quality, governance, API design, and integration of AI/ML capabilities into data workflows.

Key Requirements:

• 10+ years of experience in data engineering, ML engineering, or data platform development, including 2+ years designing or operating production data products or data services.
• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field; master's degree preferred.
• Expert-level SQL and strong Python programming skills.
• Experience with AWS, Azure, or GCP data platforms, including Amazon Redshift, AWS Glue, Azure Synapse, Azure Data Factory, BigQuery, Dataflow, Databricks, or Snowflake.
• Production experience with Apache Kafka, Amazon Kinesis, Apache Flink, or Spark Structured Streaming.
• Strong understanding of data modeling and architecture patterns, including data vault, lakehouse, and data mesh.
• Experience with data quality frameworks, data catalogs, data lineage, API design, data contracts, and event-driven architectures.
• Experience integrating ML models into data pipelines or developing AI-powered data services.
• Experience with data access controls, attribute-level masking, audit logging, and governance.
• Strong architectural leadership, technical documentation, mentoring, and cross-team collaboration skills.

Preferred Experience:

• Data mesh, federated data architecture, and enterprise data product initiatives.
• LLM applications for text-to-SQL, document extraction, semantic search, and automated enrichment.
• Government, public sector, or highly regulated environments.
• Data privacy and security requirements, including PII protection, CJIS, HIPAA, and IRS Publication 1075, where applicable.
• NIST AI Risk Management Framework and responsible AI practices.
• Data platform FinOps, cloud cost attribution, storage tiering, and query optimization.