Lead AI Engineer
About The Position
The Lead AI Engineer designs, develops, and deploys scalable generative AI and conversational AI solutions for enterprise environments. The role leads implementation of LLM-powered applications, RAG architectures, multi-agent workflows, and AI-driven APIs, with a focus on security, reliability, and performance. The engineer works with product, engineering, and business stakeholders to advance adoption of AI technologies that deliver measurable business value. The role is based at a client site in Dallas, Texas, 2–3 days per week. The stated salary range is $133,600 to $170,600 in base salary across U.S. locations; it is not adjusted for state differentials and does not include benefits or potential variable compensation. Applications are accepted until the position is filled or the posting is removed.
Requirements
- 3+ years of experience in AI/ML engineering, software engineering, or solution architecture, including 1+ year focused on generative AI, LLMs, or conversational AI.
- Strong hands-on experience with Python, FastAPI, Google Cloud Platform (GCP), and Google ADK.
- Experience designing and implementing enterprise chatbots and agentic AI solutions.
- Deep expertise in RAG architectures, knowledge bases, embeddings, and vector databases.
- Strong understanding of retrieval techniques, including hybrid search, semantic search, and BM25.
- Experience building multi-agent and agent orchestration workflows using frameworks such as Google ADK, LangGraph, Copilot Studio, or similar.
- Knowledge of AI evaluation methodologies (Evals), including techniques for measuring accuracy, grounding, hallucination rates, and response quality.
- Familiarity with API design, integration patterns, observability, resiliency mechanisms, and circuit-breaker patterns.
- Experience with prompt engineering and meta prompting.
- Experience with approaches to fine-tuning or reinforcement learning.
- Understanding of agent memory usage.
- Experience with vector databases.
- Excellent communication and collaboration skills, with the ability to work across engineering, product, and business teams.
Nice To Haves
- Experience in healthcare, patient engagement, or regulated industry environments.
- Experience with production monitoring and continuous improvement of conversational AI systems using real-world interaction data.
Responsibilities
- Design, develop, and deploy scalable generative AI and chatbot solutions for production environments, ensuring security, performance, and reliability.
- Build Python-based APIs and agentic workflows using Google ADK, FastAPI, LangGraph, Copilot Studio, or similar frameworks.
- Implement and optimize RAG architectures, vector databases, and knowledge retrieval systems to improve chatbot accuracy and reduce hallucinations.
- Develop and maintain backend services that expose AI capabilities through secure, scalable APIs.
- Design agentic AI patterns such as supervisor/orchestrator, fan-in/fan-out, retrieval, research, and evaluation workflows.
- Establish observability, monitoring, and evaluation frameworks to measure chatbot performance, response quality, and business outcomes.
- Evaluate emerging AI models, tools, and platforms and recommend best practices for enterprise adoption.
- Collaborate with product, engineering, and business stakeholders in a spec-driven development environment.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
