Quick overview
SeniorityMid Senior
Work modeOn Site
LocationLinthicum Heights, MD, United States
SQLAWSFlinkSnowflakeApacheBigQueryDatabricksGoogle CloudHIPAAKafkaLLMPythonRedshiftVaultAzureAzure Data Factory
Job description
Senior AI/ML Data Platform EngineerLocation: Linthicum Heights, MD
Work Arrangement: Onsite
Number of Positions: 1
Work Arrangement: Onsite
Number of Positions: 1
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 Google Cloud Platform 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.
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