Job Details

Primary Skills

For exp. 8-12 the pay rate is $75 and for 15+ - $80 (max.)

Job Title: AI Data Engineer, Job id - RQ00169

 Duration: 6+ month Contract with extension

Location: : Rockville, MD or McLean, VA (Hybrid)
Client: Finra

Visa Restriction: All Visa

MOI: Assessment – Virtual- In person

·       Only Local VA/MD who can take Assessment before Submission

·       Financial Background

Overview
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 data
• Participate in threat modeling and security reviews

Support compliance requirements in regulated or security-conscious environments