I previously wrote about our AI Engineering Skills Map, with the highest level skills being (i) Building and deploying AI applications, (ii) Software engineering fundamentals, (iii) Using coding agents, and (iv) Shaping the build. In this article, I will flesh out the first of them.
Being skilled at building and deploying AI applications means knowing:
LLM foundations
Grounding models with data
Building agentic systems
Evaluation-driven development
Operating in production
Machine learning foundations
This map of skills was formed by analyzing a large number of job postings, structured expert interviews, and survey responses.
The key difference between AI applications and non-AI software is that the former’s output is less predictable. You don’t know in advance what an LLM will output, or what predictions a supervised learning algorithm will make. Because of this uncertainty, building AI systems is a much more iterative process than building traditional software — it is harder to plan the process in advance. Skilled AI engineers repeatedly build a piece of software, examine it, and decide what to try next, taking a sequence of steps that are highly influenced by the intermediate results. Being able to skillfully decide what to do next allows you to create reliable software systems based on unreliable AI components. This requires knowing:
LLM foundations. Understanding how large language models tokenize input and generate output allows you to understand when to count on them and when they may fail. It also allows you to understand when to use a multimodal model, how to make tradeoffs on what to include in the context window, and reason about cache hits, knowledge cutoff, reasoning effort level, sampling parameters, and when to use special features such as tool calling. Understanding these foundations helps you choose the right model or mix of models and apply specialized techniques when needed, such as fine-tuning or self-hosting models.
Grounding models with data. LLMs require good input context to produce useful outputs. RAG using vector search was an early attempt to give LLMs relevant context, but the set of techniques for grounding models with data has grown significantly. For example, you will have to decide what to include in a prompt vs. what to let an LLM retrieve on demand using tools, and which representation fits the data and search queries: a vector index, a knowledge graph, or a semantic layer over structured data (such as customer records). You’ll also turn documents (text, PDFs, HTML, images) into LLM-ready inputs and engineer pipelines to keep data clean and fresh. When you understand the menu of techniques available to get data, you are better able to give your LLM relevant context.