AI​/ML Data Engineer

Central Strategies is seeking an AI/ML Data Engineer to support the U.S. Coast Guard. This position supports the Coast Guard’s Digital Transformation Strategy by designing, building, deploying, and scaling secure data platforms and production-grade data pipelines that enable Artificial Intelligence (AI), Machine Learning (ML), advanced analytics, and data-driven automation.

The AI/ML Data Engineer will work closely with data scientists, ML engineers, cloud engineers, enterprise architects, cybersecurity professionals, automation engineers, mission stakeholders, and program leadership to transform complex data from multiple sources into trusted, reusable, and high-performance data products for AI/ML and analytics solutions.

This role supports an enterprise automation and data modernization program focused on lakehouse architecture, cloud-native data integration, MLOps, data governance, workflow modernization, and reliable delivery of mission-ready data capabilities across the U.S. Coast Guard.

Work Location:

Hybrid in the Washington, DC metropolitan area.

Key Responsibilities

• Design, build, test, deploy, and maintain scalable data platforms that support Coast Guard AI/ML, analytics, and mission operations.
• Engineer reusable data services and curated data products for predictive analytics, generative AI, natural language processing, anomaly detection, and decision-support solutions.
• Translate AI/ML use cases into reliable data architecture, storage, processing, and integration requirements.
• Support the transition of AI-enabled minimum viable products (MVPs) and pilots into secure, production-scale capabilities.
• Develop and optimize batch and streaming ETL/ELT pipelines for structured, semi-structured, and unstructured data.
• Build automated ingestion, transformation, validation, and publishing workflows across multiple Coast Guard systems and repositories.
• Implement orchestration, monitoring, logging, error handling, and recovery mechanisms for reliable production data pipelines.
• Apply software engineering best practices, including modular design, version control, automated testing, documentation, and code review.

Databricks & Lakehouse Engineering

• Design and implement Databricks lakehouse solutions using Apache Spark, Delta Lake, Unity Catalog, notebooks, workflows, and SQL warehouses.
• Develop scalable data models and medallion architectures that support analytics, feature engineering, model training, and model inference.
• Optimize Spark workloads, data partitioning, storage layouts, and compute utilization for performance, reliability, and cost efficiency.
• Integrate Databricks with cloud storage, databases, APIs, event streams, business applications, and enterprise data services.
• Implement governed data access, lineage, metadata management, and reusable data-sharing patterns.

ML Ops & Production Integration

• Build and maintain data and feature pipelines that support model development, training, evaluation, deployment, monitoring, and retraining.
• Collaborate with data scientists and ML engineers to operationalize models, RAG pipelines, vector search, model endpoints, and generative AI applications.
• Integrate CI/CD, infrastructure as code, automated testing, containerization, and release controls into AI/ML data engineering workflows.

Data Quality, Security & Governance

• Implement automated data-quality rules, reconciliation, observability, and monitoring to ensure accurate, complete, and trusted data.
• Apply Federal security, privacy, access-control, audit, retention, and governance requirements throughout the data lifecycle.
• Diagnose data, pipeline, and platform performance issues and implement durable corrective actions.
• Participate in Agile planning, backlog refinement, sprint reviews, technical demonstrations,…