AI / LLM Integration Engineer
I ship AI features into real products, not demos — OpenAI/LLM integration, retrieval (RAG), and agentic workflows wired into production SaaS with cost control, evals, and guardrails. I've used AI integration to cut support volume 30%.
What it is
AI/LLM integration means wiring large language models into an application to do real work: answering with your own data (RAG), automating workflows, drafting content, or powering assistants — through APIs like OpenAI, with the surrounding engineering (retrieval, streaming, caching, evaluation) that makes it reliable.
Why it matters
The model is the easy part; the engineering around it is the product. Cost, latency, hallucination, prompt injection, and evaluation are what separate a flashy demo from a feature users trust. That's an engineering problem, which is where I work.
My experience
At Algotix AI I integrated OpenAI APIs into a production SaaS and reduced support tickets by 30%. I build AI features with agentic tooling (including Claude Code) in my own delivery workflow, and I design RAG/assistant features with explicit cost and quality controls.
Projects
Architecture decisions & trade-offs
Ground answers in your own data with retrieval before generation; cite sources; cap context to control cost and drift.
Cache aggressively, pick the smallest model that passes evals, stream responses for perceived speed, and budget tokens per feature.
Treat model output as untrusted input: validate, constrain with schemas/tools, and defend against prompt injection.
- Shipping LLM features with no evals — you can't improve what you don't measure.
- Using the biggest model everywhere and watching the bill explode.
- Trusting model output directly without validation or guardrails.
- Define evals before launch; track quality over time.
- Right-size the model per task; cache and stream.
- Ground with retrieval and cite sources.
- Constrain output with schemas/tools; defend against injection.
FAQ
How do you add AI to an existing SaaS without runaway costs?
Start with a narrow, high-value use case; ground it with retrieval; cache; pick the smallest model that passes your evals; and budget tokens per feature. Cost control is a design choice, not an afterthought.
When should I use RAG vs fine-tuning?
Use RAG when answers must reflect your current, specific data (most business cases) — it's cheaper, updatable, and citable. Fine-tuning suits fixed style/format needs, not fresh facts.
Key takeaways
- The engineering around the model is the product, not the model.
- Control cost and quality with retrieval, right-sized models, caching, and evals.
- Treat LLM output as untrusted; validate and guard.