Christiam Ipanaque

Professional History

I am an Artificial Intelligence Engineer based in Seattle, Washington. My career began in full-stack and backend software engineering, where I developed the engineering discipline that underpins my approach to AI: systematic debugging, production observability, and the understanding that a system is only as good as its failure modes.

Over my career, I transitioned from traditional software engineering into AI engineering, building and deploying production AI systems across the modern stack: vector search and embedding pipelines, LLM API integrations, LangChain and LangGraph orchestration, agentic system design, cloud deployment on AWS, and the observability and evaluation infrastructure that keeps production systems reliable.

I have worked alongside engineering teams solving problems that only appear when AI systems meet real users, real data, and real failure modes. Every technique, architecture decision, and failure mode warning I cover comes from a system I personally built, operated, or repaired in a production environment.

Technical Expertise

My technical work spans the full modern AI stack: RAG pipeline architecture, embeddings and vector search, LLM APIs and prompting, LangChain and LangGraph state machines, agentic system design, model fine-tuning with LoRA, and cloud deployment on AWS with CI/CD, observability, and cost controls. I am equally focused on the engineering practices that make AI systems reliable in production: rate limiting, semantic caching, automated evaluation, and incident response.

What Students Gain From My Courses

I do not teach from a textbook. Every technique I demonstrate, every architecture pattern I recommend, and every failure mode I warn about comes from a system I personally built, operated, or repaired in a production environment serving real users.

• Production mindset: how to build AI systems that survive contact with real data, real users, and real failure modes. I have shipped systems handling 12,000+ queries and 15,000+ documents per month.
• Diagnostic ability: how to locate problems when observability is broken, a stakeholder is pressing for answers, and the cause could be anywhere from the prompt to the index to the infrastructure.
• Trade-off fluency: how to make decisions between capability, cost, latency, and safety under deadline. I have redesigned architectures mid-deployment to meet cost targets without sacrificing quality.
• Professional judgment: how to recognize when the technically fastest path is the professionally wrong one. I have caught data-handling flaws in architecture reviews and insisted on disclosure during incidents.
• End-to-end ownership: how to own a system from ambiguous requirement to deployed production service with monitoring, evaluation, and cost controls.