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.
