Global Scalable Technologies

Job Description 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.