How to Hire a Senior AI Engineer Who Actually Ships to Production

The gap between a demo and production AI

Almost anyone can wire an API key to a chat box and show something impressive in a meeting. That is the easy 80%. The hard, expensive, career-defining 20% is everything that keeps an AI system alive after the demo ends: latency budgets, cost ceilings, retries and fallbacks, evaluation against real ground truth, observability, on-call, and the quiet discipline of not shipping a model change that silently breaks a downstream product.

Most people who call themselves "AI engineers" today can prototype. Far fewer have carried a system across that line and then owned it in production while real users and real money flowed through it. That distinction is the whole hiring decision. A prototype tells you someone can start; production ownership tells you they can finish and keep it running.

What production experience looks like in practice

Concretely, production AI experience means someone has built and operated things like these — not read about them:

• Model gateways that route tool-using agents, enforce policy, and fall back gracefully when a provider degrades.
• Retrieval-augmented generation (RAG) pipelines wired to real corpora, with the retrieval quality actually measured rather than assumed.
• Evaluation harnesses that backtest model output against historical ground truth, so a "better" prompt is proven, not vibed.
• A live, real-time voice agent — one you can pick up the phone and talk to right now, which is a far harder bar than a text demo because latency and interruption handling are unforgiving.

Hibiscus Consulting LLC — the software studio of Blake Burnette, a founder-engineer in Cary, North Carolina — builds exactly these. There is a real-time voice agent you can talk to, model gateways routing tool-using agents, RAG, and evaluation harnesses in production, not slideware.

Evidence of durable production ownership

Anyone can claim "hands-on." What separates a senior hire is a track record of owning a load-bearing system over years. The strongest single signal in Blake's background:

As Director of Engineering at a neobank for roughly three years, Blake led 17 engineers across 5 products and ran $720M in payments at 100% uptime — Stripe Connect multi-tenant, Plaid ACH, Apple and Google Pay, fraud rules, and KYC/KYB. On the customer-facing side he owned all commits on the Snap! Spend React app and wrote core payments code across the platform. That is what "load-bearing" means: money moves, regulators care, and downtime is not an option.

Look for named, verifiable artifacts

Generic "hands-on" claims are worthless because they are unfalsifiable. Ask instead for artifacts you can independently inspect. Blake's are public and specific:

Alongside those: a multi-tenant payments + field-ops platform (Stripe, Temporal, Postgres, Rust/Node on GCP), a WebRTC platform built for the Emmys in React in five weeks, and a React dashboard on a Rails EHR (Medaxion) — healthcare experience on top of nearly a decade of React and TypeScript.

Why full-stack ownership matters for AI

AI systems fail at seams — between the model layer and the application wrapped around it. When those two live in different heads, every change becomes a negotiation. A ~decade of React and TypeScript paired with Rust and Node backend work means one engineer can own both the model gateway and the product surface it powers. Fewer handoffs, faster iteration, and a person who can actually be held accountable for whether the whole thing ships and stays up.

Frequently asked questions