AI / LLM Developer

What the role actually does

Where the architect thinks in blueprints, the developer thinks in working code. This is the hands-on engineering lane: taking a model that can generate text and turning it into a reliable feature a business can depend on. It suits people who like building, debugging, and the satisfaction of shipping something that works.

Building applications on top of models.

The core of the job is integrating a language model into real software: calling it correctly, handling its responses, connecting it to the data and tools it needs, and making the whole thing robust enough for production traffic. Most of this is ordinary strong engineering applied to a new kind of component.

Making models do more than chat.

Modern AI development is increasingly about systems that act, not just answer: giving a model tools it can call, grounding it in a company’s own data, and building agents that carry out multi-step tasks. This is where the interesting engineering lives, and where most of the current hiring is concentrated.

Making it reliable, fast, and affordable.

A working prototype is the easy part. The developer’s real value is in everything after: handling errors and rate limits, controlling cost and latency, testing that outputs are correct, and keeping the system secure. This is the engineering discipline that separates a demo from a product.

What to learn, in order

A sensible order, from the basics of working with a model to the engineering that makes an application production-ready. Each concept below will link to a full explanation as our concept library grows.

1. 1
   Working with a model through its API. The foundation: how to call a language model in code, what a prompt is, how context works, and the mechanics of the Messages API, including streaming and handling responses.
2. 2
   Grounding and extending the model. The patterns that make a model useful on real data: retrieval-augmented generation (RAG) to answer from your own documents, and tool use to let the model take actions in the world.
3. 3
   Building systems that act. Moving from single calls to autonomous behavior: the difference between a workflow and an agent, how the tool-use loop works, and connecting to external systems through the Model Context Protocol (MCP).
4. 4
   Engineering for production. What separates shipped software from a script: error and rate-limit handling, evaluation and testing of model outputs, cost and latency optimization, and security against risks like prompt injection.
5. 5
   Working effectively with AI coding tools. The modern developer also builds with AI: using tools like Claude Code well, and knowing where generated code needs careful review.

Where certification fits, and where Sentievo helps

Once you can build and ship with a model, the question becomes how you prove it. For this role, the recognized credential is Anthropic’s Claude Certified Developer.

Ready to turn this into a schedule? Build a free, personalized study plan for any Claude certification: choose your weeks and weekly hours to get a phased roadmap through our concepts, diagnostics, and mock exams.

Recommended resources

The most useful preparation is a mix of understanding the concepts and building real things.

• Sentievo’s concept library (growing): original, plain-language explanations of the building blocks above, written to be clearer and shorter than what exists elsewhere.
• Sentievo’s free Developer diagnostic: a no-cost way to see how close you are to exam-ready and where your gaps are, before preparing in full.
• Anthropic Academy: Anthropic’s own free, self-paced courses, the authoritative source for how Claude’s API, tools, and platform actually work.