AI engineering is the discipline of turning models into products - and that is the work I do every day. From LLM application engineering and model integration to evaluation and deployment, I build AI features that are accurate, fast, observable, and ready for production.
Machine learning is only useful when it ships. My focus is the engineering around the model: data flow, evaluation, latency, cost, and the interfaces that let AI slot into a real product.
- AI engineering is the discipline of turning models into production products.
- Most of the value lives outside the model call: context, output schemas, evals, latency, and cost.
- Evaluation is first-class - golden datasets and automated evals let you change prompts and models with confidence.
- The goal is the right tool shipped well, whether that is an LLM or classic machine learning.
LLM application engineering
Prompt systems, structured outputs, function/tool calling, streaming, and caching - engineered for accuracy, latency, and cost.
Model integration
Integrating Claude, OpenAI, Gemini, and open models behind clean interfaces, with routing and fallbacks across providers.
Evaluation & quality
Eval suites, golden datasets, and regression tracking so model behavior is measured, not guessed - before and after each change.
Production readiness
Observability, token-cost control, rate limiting, and safe rollout patterns so AI features hold up under real traffic.
The engineering around the model
Most of the value in an AI feature lives outside the model call: how you prepare context, enforce output schemas, evaluate quality, handle failures, and control cost and latency. AI engineering is that surrounding system, and it is what separates a flaky prototype from a feature you can ship.
I treat evaluation as a first-class part of the build. With golden datasets and automated evals, you can change prompts and models with confidence instead of crossing your fingers.
From prototype to production
A typical engagement moves a promising prototype to production: tighten the prompts and outputs, add evals and tracing, control token cost, harden error handling, and wire the feature cleanly into your stack. Where classic ML fits better than an LLM, I use it - the goal is the right tool, shipped well.
The outcome is AI that behaves predictably, scales with traffic, and keeps improving as you measure it.
What does an AI engineer do?
An AI engineer turns models into products - designing prompt and retrieval systems, integrating models, building evaluation and observability, and handling latency, cost, and reliability so AI features work in production.
What is the difference between AI engineering and machine learning?
Machine learning builds and trains models; AI engineering builds the systems around them - application logic, evaluation, integration, and production concerns. Modern LLM products lean heavily on AI engineering rather than training from scratch.
How do you make sure an AI feature is reliable?
With evaluation as a first-class practice: golden datasets, automated evals, tracing, and regression tracking, combined with structured outputs, fallbacks, and cost and latency controls.
Ready to build with AI?
Tell me what you are building. I will map the fastest path from idea to a system you can trust in production.
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