Engineers and engineering leaders who build, deploy, and scale software systems.

Applying AI tools and techniques to professional workflows — assign alongside more specific AI topics.

Building autonomous AI agent systems that take actions, use tools, and orchestrate multi-step workflows.

Building technical literacy — understanding APIs, databases, system architecture, and engineering tradeoffs.

Designing scalable, reliable software systems — architecture patterns, distributed systems, and technical tradeoffs.

Measuring and evaluating AI model performance, quality, and reliability for production use cases.

Operating and deploying large language models in production — fine-tuning, inference, monitoring, and cost optimization.

Building retrieval-augmented generation and search systems — vector databases, embeddings, ranking, and retrieval pipelines.

How to build and ship a complete AI product yourself: frontend, agent logic, a model you host, caching, and deployment.

Founder | Ex-Google | Prof UCLA & UMN

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