Senior AI​/ML Data Platform Engineer

At 4A Consulting, we turn complexity into opportunity. Founded in 2014 and headquartered in Baltimore, MD, we are a women-owned, boutique consulting firm specializing in delivering innovative, data-driven solutions to both the Federal Government and Fortune 500 clients. Our team blends deep industry knowledge with advanced technologies to design tailored strategies that drive measurable and sustainable outcomes. We pride ourselves on an agile, collaborative approach that helps organizations navigate challenges and seize emerging opportunities in a rapidly evolving digital landscape.

At 4A, we don’t just deliver projects, we build trusted partnerships that empower our clients to lead with confidence in the digital age.

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 supporting multiple state agencies. The role focuses on scalable data platforms, real-time streaming, data quality, governance, API design, and integration of AI/ML capabilities into data workflows. The engineer will establish architectural standards, ensure reliable and secure data delivery, mentor engineering teams, and enable agencies to build reusable, trustworthy, AI-ready data products.

• Data Product Strategy and Architecture:
  Define data product standards, lifecycle management, ownership, quality tiers, and platform roadmaps. Design data APIs, schema versioning, data contracts, SLAs, and deprecation policies. Promote data mesh and federated data architectures where appropriate.
• Real-Time and Streaming Data:
  Architect batch and streaming pipelines, event-driven processing, and low-latency feature delivery for online ML inference. Establish standards for throughput, scalability, back pressure handling, consumer management, and disaster recovery.
• Data Quality and Governance:
  Implement data quality frameworks, automated validation, scoring, lineage tracking, and monitoring. Enforce data access controls, attribute-level masking, and audit logging in collaboration with governance, privacy, and security teams.
• AI-Powered Data Services:
  Design and implement intelligent entity matching, data classification, anomaly detection, semantic search, automated enrichment, ML-based scoring, and LLM-powered data transformation and summarization. Establish evaluation and monitoring practices for accuracy, fairness, and reliability.
• Technical Leadership:
  Own data platform architecture decisions, mentor engineers, lead cross-agency technical collaboration, and represent the platform in architecture reviews, governance councils, and vendor evaluations.
• Documentation and Optimization:
  Maintain architecture decision records (ADRs), data product standards, and operational runbooks. Improve platform scalability, data reuse, query performance, and cloud cost efficiency.

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field from an accredited college or university. A master's degree is preferred.
• At least ten (10) years of experience in data engineering, ML engineering, or data platform development, including at least two (2) years designing or operating production data products or data services.
• At least twelve (12) years of computer experience in information systems design, as required by the stated contract criteria.
• Expert-level SQL and strong Python programming skills, with experience building reliable, production-grade data systems.
• Experience with cloud data platforms on AWS, Azure, or GCP, including services such as Amazon Redshift, AWS Glue, Azure Synapse, Azure Data Factory, Big Query, Dataflow, Databricks, or Snowflake.
• Production experience with streaming technologies such as 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.
• Hands-on experience with data quality frameworks, data catalogs, data lineage, API design, data contracts, and event-driven architectures.
• Demonstrated experience integrating ML models into data pipelines or developing AI-powered data services.

• Experience leading or contributing to data mesh, federated data architecture, or enterprise data product initiatives.
• Familiarity with LLM applications for structured and semi-structured data, including text-to-SQL, document extraction, semantic…