How Do I Build Production AI Systems?

Demo AI optimizes for impressive one-off results. Production AI optimizes for reliability, cost efficiency, and maintainability at scale. In production, you need to handle rate limits, manage costs (often $10K+/month in API calls), implement graceful degradation when services fail, add comprehensive logging and monitoring, ensure security and compliance, and design for 99.9% uptime. These concerns don't exist in demos, which is why the transition feels so jarring.

The top production challenges in 2026: 1) Cost management—LLM API costs can explode without caching, batching, and model routing strategies. 2) Latency—users won't wait 10 seconds for a response, so you need streaming, async processing, and smart UX patterns. 3) Observability—when AI outputs go wrong, you need traces, evaluations, and monitoring to diagnose issues. 4) Reliability—handling API failures, rate limits, and model degradation gracefully. 5) Security—prompt injection, data leakage, and compliance requirements.

Most courses focus on model training, not production deployment. Your best resources: 1) Study open-source production systems (LangChain, LlamaIndex architectures). 2) Read postmortems from companies running AI at scale. 3) Build side projects that force you to handle real constraints. 4) Work with a coach who's actually deployed production AI—they can teach you patterns in weeks that take months to discover alone. The fastest path is learning directly from practitioners.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

Start small but real: 1) Choose a project with actual users (even if it's 10 people internally). 2) Set up observability from day one—logging, cost tracking, latency metrics. 3) Implement caching early (you'll thank yourself later). 4) Design for failure: what happens when the API is down? 5) Add rate limiting and cost caps before you need them. 6) Deploy incrementally with feature flags. The goal isn't perfection—it's learning the production loop of deploy, monitor, fix, improve.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.