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Lead and mentor a San Diego engineering team while staying hands-on building production LLM systems (RAG, agents, document processing). Drive architecture, evaluate models and tooling, own end-to-end delivery, collaborate with clients, and create reusable internal tooling and best practices to improve team performance and client outcomes.

Lead client discovery, define and prioritize AI use cases, analyze workflows, and design AI prototypes and process improvements. Collaborate with engineers to deliver solutions, manage project timelines and quality, train clients, support adoption, gather feedback, and iterate on outcomes. The role also requires tracking project progress, time, utilization, and efficiency across multiple client engagements.

Build and deploy production AI systems, including backend AI modules, agentic workflows, advanced RAG pipelines, vector database solutions, and retrieval infrastructure. Own infrastructure, containerized deployments, testing, observability, cost monitoring, and production reliability. Abstract reusable internal tools across client engagements, optimize AI systems for accuracy, latency, and cost, and collaborate closely with AI strategists to deliver client solutions.

Lead live AI training and hands-on workshops for executive and frontline client teams. Design curricula, training products, templates, delivery playbooks, and repeatable enablement systems using adult learning principles. Adapt technical AI concepts for nontechnical audiences, manage concurrent client engagements, gather delivery insights, and improve training processes. The role combines client facilitation with building Cadre AI’s scalable training practice and requires San Diego-based collaboration and travel.

Build and ship production-grade AI systems: backend AI modules, agentic workflows, and optimized RAG/retrieval pipelines. Own end-to-end delivery, deployment (IaC, Docker, Kubernetes), observability, testing, and internal tooling. Improve retrieval accuracy and latency, document reusable abstractions, and collaborate closely with strategists to deliver client-facing AI solutions.

Triage AI opportunities, shape technical solutions, write precise SOWs and internal handovers, identify reuse vs. new engineering, and coordinate sales and engineering to deliver scoped AI engagements (5–10 concurrently, 30–45 day cycles). Ensure delivery-ready documentation and flawless handoffs to implementation teams.

Lead and mentor the Colombia engineering team while remaining hands-on building production LLM systems: agentic workflows, RAG/document pipelines, extraction, APIs, data pipelines, and cloud infrastructure. Drive architecture and technical direction, ship reliable production systems, evaluate models and tools, create reusable tooling, and represent technical decisions to clients. Own delivery outcomes and engineer growth.

Embed with clients to discover high-impact AI opportunities, design and build production LLM/RAG/agent systems, deploy secure cloud services, create evaluation and observability tooling, and scale repeatable playbooks while managing client relationships and mentoring junior engineers.