AI Engineering Leadership: From Prototype to Production

Identify high-value AI opportunities and avoid AI for AI's sake

Turn an AI proof of concept into a reliable product

Build vs. buy, model selection, cloud, RAG, agents

Design multi-agent workflows, tools. Make decision when to enable human oversight vs autonomous operation

Data pipelines, evaluation frameworks, hallucination and reliability. Defining meaningful metrics

Cost optimization, latency, observability, security

Communicate ROI, risk, trade-offs and investment to CFOs and boards