AI Data Engineer

Title : AI Data Engineer

Location : Rockville, MD or McLean, VA

Target Start Date : SAP

Type : contract

Pay Rate: DOE


The AI Data Engineer is responsible for designing and implementing data pipelines and retrieval systems that power a modern generative AI platform. This role focuses on ingesting, transforming, and indexing domain data to enable accurate, context-aware responses from AI applications. The engineer partners with platform and application teams to continuously improve data quality, retrieval performance, and overall system effectiveness.


Key Responsibilities

Data Engineering & ETL

• Design and build scalable ETL pipelines for structured and unstructured data sources
• Develop robust ingestion workflows for document parsing, transformation, and loading
• Implement data validation and quality checks to ensure accuracy and completeness
• Build and operate data pipelines using cloud-based services and distributed architectures

RAG Pipeline Development & Search Optimization

• Design and optimize retrieval-augmented generation (RAG) pipelines, including chunking, embedding generation, and retrieval logic
• Tune search relevance using vector databases and search technologies
• Evaluate retrieval effectiveness using benchmarks and establish measurable quality metrics
• Experiment with embedding models, hybrid search approaches, and ranking strategies to improve results

Quality & Testing

• Implement testing strategies for data pipelines, including validation of ingestion accuracy and transformation logic
• Develop automated tests to detect regressions in retrieval quality
• Build and maintain evaluation benchmarks measuring precision, recall, and relevance
• Promote test-driven development (TDD) practices across pipeline and integration work

Generative AI & Emerging Technologies

• Stay current with advancements in RAG architectures, embedding models, and retrieval techniques
• Identify opportunities to enhance retrieval quality using emerging methods (e.g., reranking, hybrid search)
• Collaborate with engineering teams to ensure data systems support high-quality AI outputs

Security & Compliance

• Follow security policies and best practices for data handling and infrastructure
• Implement secure coding practices, especially when working with sensitive dat
• Participate in threat modeling and security reviews
• Support compliance requirements in regulated or security-conscious environments

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