Christiam Ipanaque
AI Engineer & Instructor
Author of Build and Deploy Production AI Systems.
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 six years of professional practice, I transitioned from traditional software engineering into AI engineering, building and deploying production AI systems across the modern stack: LLM API integration, vector search and embedding pipelines, 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.
Production Scenarios
Every engineer accumulates war stories. These are scenarios I have faced in production. Documented, measurable, and mapped to real engineering competencies.
Building an Agent from a Two-Sentence Request
A client requested "an AI that qualifies leads" with no data, schema, or success criteria. Christiam scoped requirements through stakeholder interviews, built a LangGraph-based agent with iterative refinement and human-in-the-loop validation, and shipped a system processing 5,800+ leads per month with a 47% increase in qualified conversions.
Diagnosing a Degraded RAG Pipeline in Production
A deployed RAG system lost 40% of its retrieval quality overnight. Observability tooling was partially broken and the regression was intermittent. Christiam repaired enough measurement to reason safely, isolated the cause to a shift in embedding distribution after a model provider deployment, and deployed a fix before a premature rollback could be ordered.
Choosing Between Latency, Cost, and Accuracy at Scale
A real-time LLM agent was running 6x above its per-request cost projection. Christiam redesigned the architecture mid-deployment by introducing semantic caching, model tiering for routine versus complex queries, and structured output routing. Cost dropped 70% while response quality was maintained.
Building a Measurement System Where None Existed
A customer support AI had been deployed for months with no one able to measure its quality. Christiam designed automated evaluation harnesses for answer correctness, retrieval precision, escalation rate, and user satisfaction. A baseline was established where none had existed, enabling iterative improvement against real metrics.
Catching a Data-Handling Flaw in a Peer's Design
During an architecture review, Christiam identified that a proposed agent system would exfiltrate personally identifiable information through its tool-calling path. He documented the flaw, proposed a guardrail layer with output filtering and input sanitization, and escalated the risk before the system reached production.
Handling a Customer Data Exposure During an Outage
A production incident caused an LLM to surface one customer's data in another customer's session. The fastest technical path was a prompt hot-patch. Christiam insisted on full disclosure, coordinated the incident report with the affected party, and drove an architecture change to prevent recurrence: tenant isolation via separate embedding indexes and retrieval-scoped authorization.
Technical Expertise
My technical work spans the full modern AI stack: LLM APIs and prompting, embeddings and vector search, RAG pipeline architecture, 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.
Areas of Instruction
I teach across the full AI engineering curriculum, covering the modules where production depth matters most. Each course is assessed through practical coding assignments and scenario-based evaluations.
LLM APIs & Prompting
- OpenAI SDK Integration
- Prompt Engineering Patterns
- Parameter Tuning
- Streaming Responses
- Structured Outputs
RAG Pipelines
- Chunking Strategies
- Document Parsing
- Vector Database Setup
- HyDE Re-ranking
- Multi-Modal RAG
LangChain & LangGraph
- Chains, Prompts & Memory
- Tool Use & Function Calling
- State Graphs with LangGraph
- Human-in-the-Loop
- Multi-Agent Orchestration
Agentic Systems
- ReAct Agent Pattern
- Tool-Using Autonomous Agents
- Planning & Decomposition
- Reflection & Self-Correction
- Handling Infinite Loops
Production & Deployment
- AWS Deployment (ECS / Lambda)
- API Design for LLM Apps
- Rate Limiting & Throttling
- Semantic Caching
- CI/CD for AI Pipelines
Observability & Evaluation
- LLM Tracing
- Automated Evaluation
- Token & Cost Monitoring
- A/B Testing LLM Configs
- AI Evaluation Methods
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.
Get in Touch
Have a project in mind? Send me a message and I will present AI automation solutions for your needs.