Generative AI Lead - Enterprise GenAI Architect & Builder

PeopleStrong; Northern (KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

MathCo is seeking a Generative AI Lead to drive the design and delivery of production-grade GenAI solutions for enterprise clients. The role spans hands-on engineering, system architecture, and technical leadership to solve complex business problems.

You will design multi-agent AI systems, implement RAG pipelines, and integrate LLM capabilities into business workflows while guiding engineering teams toward scalable, reliable deployments across cloud environments.

Qualifications

• Proven experience building and deploying LLM-based / Generative AI applications in production.
• Strong hands-on Python and modern AI frameworks (LangChain, LlamaIndex).
• Experience designing RAG pipelines or knowledge retrieval architectures.
• Exposure to multi-agent frameworks (LangGraph, CrewAI) or similar architectures.
• Experience in cloud environments (AWS, Azure, GCP) and scaling AI systems.
• Demonstrated ability to lead technical teams or initiatives.
• Strong communication skills with stakeholders across technical and business domains.

Responsibilities

• Lead the design and development of enterprise-scale Generative AI solutions.
• Architect and implement multi-agent AI systems and RAG pipelines.
• Integrate LLM capabilities into business workflows and apps.
• Partner with stakeholders to translate complex problems into scalable AI solutions.
• Mentor engineering teams and maintain high technical quality.
• Ensure reliable deployment, monitoring, and performance of AI systems.
• Optimize infrastructure across cloud environments and GPU utilization.

Job description

MathCo is seeking a Generative AI Lead to drive the design and delivery of production-grade GenAI solutions for enterprise clients. The role spans hands-on engineering, system architecture, and technical leadership to solve complex business problems.

You will design multi-agent AI systems, implement RAG pipelines, and integrate LLM capabilities into business workflows while guiding engineering teams toward scalable, reliable deployments across cloud environments.