Lead GenAI Engineer

The Company
Headquartered in Dallas TX, our client is a technology and strategy consultancy that aims to provide a competitive edge for its clients by solving complex problems with data, software, and strategy. They specialize in areas like technology strategy, product development, software engineering, and digital transformation, with a particular emphasis on AI, MLOps, and Data Engineering. The firm's clientele spans various industries, including AgTech, Healthcare, Logistics, and Financial Services.

Platform / Stack
You will work with technologies that include RAG, Agentic AI, Python, and LLMOps.

What You'll Do:

• Own the end-to-end technical quality of generative AI systems — from data preparation and retrieval infrastructure through model integration, prompt design, output evaluation, deployment, and production monitoring.
• Establish and enforce GenAI engineering standards across the team: prompt versioning and management, evaluation harness design, context window strategy, output quality testing, hallucination tracking, and system documentation requirements.
• Make final technical decisions on LLM selection, context architecture, retrieval strategy, fine-tuning approaches, and orchestration frameworks — with the judgment to know when a simpler system outperforms a complex one.
• Own the technical risk register for each engagement — identifying context poisoning risks, hallucination failure modes, latency bottlenecks, cost overruns, and compliance exposure before they surface in production.  
• Design end-to-end GenAI system architectures that integrate LLMs cleanly with enterprise data platforms, application layers, and operational workflows — built for reliability, observability, and controlled evolution.
• Architect retrieval-augmented generation (RAG) systems with rigorous attention to chunking strategy, embedding model selection, vector store design, retrieval quality evaluation, and reranking — treating retrieval as an engineering discipline, not an afterthought.
• Design agentic AI systems with well-defined tool interfaces, error handling, state management, and human-in-the-loop controls — architectures that behave predictably under real enterprise data and user behavior.
• Architect LLMOps foundations covering model gateway management, prompt registry, evaluation pipelines, A/B testing for prompts and models, cost monitoring, and production observability with output quality tracking.
• Lead and mentor a team of AI engineers and data scientists — setting technical direction, unblocking delivery, and raising the engineering quality of every individual contributor on the engagement.
• Represent the technical voice of the GenAI team in client-facing settings — communicating system behavior, failure modes, cost implications, and production risks with precision and candor.
• Establish incident response procedures for GenAI systems — owning the technical response when output quality degrades, retrieval pipelines drift, context windows overflow, or serving infrastructure fails under load. 
• Ensure all GenAI systems meet client data governance, privacy, and compliance requirements — including data residency, PII handling in context, audit logging, and prompt injection defense at the architecture level.