Principal GenAI Data Scientist

Arthur Grand Technologies Inc • McLean, VA

62d • Onsite

About The Position

Arthur Grand Technologies is currently seeking a highly motivated and skilled Principal GenAI Data Scientist for one of our clients. Client is seeking a highly experienced Principal GenAI Data Scientist to lead the design and development of AI agents, agentic workflows, and production GenAI applications that solve real business problems. You’ll be a hands-on technical leader who partners with full-stack engineers, designers, product managers, and data engineers to ship secure, reliable, and scalable GenAI solutions.

Requirements

• Hands-on ML to GenAI transition with demonstrated delivery of AI agents/agentic workflows.
• Deep experience with RAG (documents to vectors, retrieval, synthesis) and Graph-RAG.
• Strong Python (Jupyter) and modern ML stack (Transformers, LangChain/LlamaIndex or similar).
• Practical use of MCP and A2A communication patterns in real workflows.
• Cloud-native AI on AWS (SageMaker, Bedrock; MLFlow/Kubeflow on EKS).
• Vector databases/knowledge bases (AWS Knowledge Bases/Bedrock, Elastic, MongoDB Atlas, etc.).
• Proven prompt engineering, fine-tuning, evaluation frameworks, and guardrails/safety implementation.
• Built and deployed GenAI apps to production (latency, cost, observability, rollback, safety).
• Strong data engineering fundamentals: ingestion, chunking, enrichment, anonymization, and governance.
• 10+ years in AI/ML with 3+ years focused on applied GenAI/LLM solutions.
• Prior software engineering experience and ability to partner closely with full-stack teams.
• GitHub repository link required for consideration (please include recent GenAI/agent work).

Nice To Haves

• Publications or patents in AI/ML/LLM.
• Experience with enterprise AI governance and ethical deployment.
• CI/CD for MLOps and scalable inference APIs; observability and evaluation in production.
• Experience designing business use cases from problem framing through measurable outcomes.
• Multi-modal models (text/image/audio/video) and tool-use/function-calling.
• Knowledge graphs for Graph-RAG; retrieval policy and query planning.
• GenAI architectural patterns (routing, orchestration, distillation, hybrid search).

What This Job Offers

• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 11-50 employees