CASE STUDY 4
Hiring LLM Engineers to Build a Production-Ready Enterprise AI Platform
AI TALENT STRATEGY CASE STUDY
An enterprise software vendor wanted an internal AI platform other teams could build on, instead of every product team gluing its own OpenAI calls together. Good idea. Nobody internally had built platform-grade LLM infrastructure before.
Six product teams. Six thin wrappers around an LLM API. Six different prompt formats, six different ways of handling rate limits, three different vector database choices. None of it reusable. The mandate: hire a platform team to consolidate this into shared infrastructure other engineers could actually adopt, without a two-week onboarding slog attached.
This is an AI architect problem as much as an engineer problem. The team needed someone who'd made real infrastructure decisions about model routing, fallback behavior during a provider outage, and cost attribution across teams sharing the same platform.
Role breakdown: 1 AI Architect owning platform design, routing strategy, and multi-provider fallback logic. 3 LLM Engineers building the serving layer, prompt versioning, and evaluation harness. 2 NLP Engineers handling domain fine-tuning and retrieval quality against the company's own document corpus. 1 ML Engineer owning cost and latency monitoring across every consuming team.
The strongest AI architect candidate had a scar. A real one. When a primary LLM provider went down for four hours, their fallback routing kept the platform functional at reduced quality instead of fully dark. That one story told us more than a stack of certifications, and we started asking every architect candidate for their own version of it. Clearest signal in the entire search.
For LLM engineers, we weighted heavily toward people who'd built or maintained an evaluation harness, meaning a system that scores model outputs against a test set automatically, rather than relying on vibes and spot-checking. Teams without that discipline ship regressions constantly and often don't notice until a customer complains.
Onboarding the first two product teams onto the new platform took closer to six weeks than the two days the architect had originally hoped for, mostly because each team's existing prompt logic carried undocumented assumptions nobody remembered making. The platform team turned that friction into a migration checklist, and every team after the first two onboarded in under a week.
Cost attribution turned out to be almost as important to adoption as reliability. Once product teams could see their own token spend broken out by feature, two teams independently found and fixed prompts that were burning budget on redundant context, without anyone on the platform team having to ask.
7 hires across architect, LLM, NLP, and ML roles
13 weeks end to end
6 within first two quarters
Handled without customer-facing downtime
