CEOs of 40-person companies often imagine their first AI hire will be a model trainer—someone who builds custom neural networks from scratch. Reality check: that's maybe 10% of the job. The real work is less glamorous but more critical. Most days are spent cleaning data, managing third-party APIs, and translating between technical teams and business stakeholders. The actual model training happens mostly in vendor platforms like OpenAI or Anthropic. The real value is in connecting those tools to your actual operations.

Let's walk through three typical weeks in the life of an AI engineer at a small company.

Week one: Data plumbing. The team needs to connect customer support tickets to knowledge base articles. The engineer spends four days writing scripts to extract text from Zendesk, clean it of HTML tags, and structure it for retrieval. The fifth day goes to testing edge cases—what happens when a ticket has no subject line? Or when the article contains a table? No model training happens. Just making sure the input pipeline works.

Week two: Vendor management. The company wants to use OpenAI's API for summarization. The engineer evaluates three pricing tiers, tests rate limits, sets up monitoring for API failures, and writes fallback logic when the service is slow. They spend two days documenting the quirks—how the API handles technical jargon, its tendency to hallucinate dates. Then they build a wrapper that handles all this complexity so the business team doesn't need to think about it.

Week three: Translation. Sales wants a tool to draft follow-up emails. The engineer sits with the sales team for three days to understand their actual workflow—what information they need to include, what tone to use, how they handle objections. They build a prompt library that maps to different scenarios, then create a simple interface where sales reps can input key details and get drafts. The heavy lifting isn't in the model—it's in making sure the output matches how salespeople actually work.

So when you're hiring for this role, don't look for deep learning expertise. Look for someone who can build data pipelines, understand API limitations, and translate business needs into technical requirements. The best candidates have experience with ETL tools, cloud services, and have worked with non-technical teams. They should be able to explain trade-offs clearly—why a certain approach might be slower but more reliable, or how to handle missing data. The magic isn't in the models themselves—it's in making them work in your messy, real-world environment.

This piece is from our notes on helping SMBs (10-100 people) build their first in-house AI teams. If your team is exploring this — quick feedback and questions welcome in the comments.