How to hire an AI engineer who ships to production
A complete playbook — sourcing strategy, boolean strings, screening, interview stages, a technical take-home, reference checks, and a weighted scorecard. Built for B2B SaaS hiring teams.
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- Boolean sourcing strings (LinkedIn + GitHub)
- LatamCent's initial screen questions
- Hiring manager interview guide
- Technical take-home + debrief
- Exec / final interview questions
- Reference check script
- Salary bands + weighted scorecard
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Talk to LatamCent →Where to find AI engineers and what signals matter
The title "AI Engineer" is new and inconsistent. You are hunting for people who ship ML and LLM features to real users, not researchers and not people who once called an API.
Start with engineers who have taken a model from prototype to production and owned the full loop: data, retrieval, evaluation, inference, and the monitoring that catches it when the model is wrong. The strongest signal is a public artifact. A model card, an eval writeup, a benchmark repo, or a technical post tells you more than five years of generic "ML" in a job title.
In LATAM specifically, target engineers from Nubank, Mercado Libre, Globant, Rappi, and Kavak who have shipped ML into products at scale. Brazil's FAANG returnees who came back after remote-first opened up are underpriced for what they can do. Colombia's Medellin corridor and Argentina's MercadoLibre and Globant alumni are deep benches for applied AI.
Filter for AI Engineer, ML Engineer, and Applied Scientist titles with LLM, RAG, or fine-tuning in the experience description. Target alumni of Hugging Face, Cohere, Scale, Nubank, and Mercado Libre.
GitHub
Public repos using transformers, langchain, llamaindex, or vLLM with real commit history. Someone who maintains an eval harness or a fine-tuning script is worth more than a follower count.
Communities
Hugging Face forums, the LangChain and Latent Space communities, and the MLOps Community Slack. Post a hard retrieval or eval problem and watch who gives the most useful answer.
LATAM specifically
Colombia: Ruta N Medellin, Universidad de los Andes alumni. Brazil: FAANG returnees, Nubank and Itau ML alumni. Argentina: MercadoLibre and Globant engineering alumni.
Copy-paste sourcing strings
Use these on LinkedIn Recruiter, GitHub, and X. Tweak the company names to match your stack.
Time-saving move: Run the GitHub string first and find 5 to 10 active contributors, then look them up on LinkedIn. GitHub activity filters out people who only talk about AI and shows you the people who actually build with it.
The 30-minute call that cuts 70% of candidates
Run this yourself or delegate to a senior recruiter. The goal is not to evaluate depth. The goal is to confirm this person has shipped real AI features to real users.
Most candidates who apply to AI Engineer roles have notebook experience, coursework, or a side project, but nothing in production. The screen below reveals that fast. You are looking for specific stories about what they built, how they measured it, and what broke, not general claims about models they have read about.
Red flags in the screen
- Cannot describe how they measured a single result
- Only notebook, coursework, or demo experience, nothing in production
- Name-drops models but cannot go one layer deeper
- Describes work in vague team terms ("we built...")
- English breaks down under technical questioning
Green flags in the screen
- Talks in terms of evals, metrics, and tradeoffs unprompted
- Has shipped to real users, not just demos
- Reaches for the simplest thing that works
- Uses "I" not "we" when describing decisions
- Clear, confident English at conversational speed
The 60-minute depth eval
This is where you separate people who talk about AI from people who have shipped it. Block 60 minutes. Go deep on two or three areas rather than covering everything.
A 3-hour scoped take-home assignment
Keep it real. Use a problem that mirrors actual work at your company. Respect their time by being specific about scope and paying for it.
Before you send this: Tell the candidate exactly what you are evaluating (pipeline quality, evaluation rigor, honest failure analysis, and communication) and give them a hard time cap. Three to four hours max. Candidates who go 10 hours are not showing hustle, they are showing poor scope judgment, which is a bad sign for an AI engineer.
The final 45-minute conversation
At this stage you are validating judgment, long-term trajectory, and how this person operates when the problem is vague. Keep it conversational.
If your stack is LLM-heavy
Ask them to design the AI layer for a product that answers questions over each customer's private data, for 10,000 tenants. Watch how they handle multi-tenancy, retrieval at scale, cost, and isolation.
If your customers are in fintech or healthcare
Ask how they have handled data privacy and PII when building with models. Regulated AI work needs people who treat compliance and data handling as a first-class concern, not a legal team problem.
If you have a fast-moving roadmap
Ask how they ship an AI feature behind a flag, measure it on real traffic, and decide to roll forward or back. Look for evals on live data, not just a launch and hope.
If remote collaboration is critical
Ask what their async standards are. The best AI engineers document their experiments and decisions. Ask to see an eval writeup, a Notion page, or a PR description they are proud of.
The reference call that actually tells you something
Call two references. One former manager and one former peer or teammate who worked closely with them. Do not accept written references only.
Opening frame: Say you are not looking for a performance review. You want to understand how this person works so you can set them up for success. This gets you more honest answers because references feel less like they are evaluating the candidate and more like they are advising you.
Salary benchmarks and the weighted scorecard
LATAM AI engineer salaries vary by country, seniority, and English fluency. These ranges are based on LatamCent placements and market data. All figures in USD per month. Toggle between mid-level and senior.
Pricing tip: Proven LLM production experience, a strong eval track record, and B2+ English add 15 to 25% to the base. Budget for it. The gap between an engineer who ships measured AI features and one who ships demos is the whole job.
| Criteria | What good looks like | Weight | Score (1–5) |
|---|---|---|---|
| ML / AI engineering depth | Real applied judgment on models, retrieval, and evaluation. Reasons from data, not hype. | 30% | |
| Production deployment and MLOps | Has shipped to real users. Thinks about latency, cost, monitoring, and rollback. Writes clean code. | 20% | |
| LLM / applied AI experience | Hands-on with RAG, fine-tuning, prompting, and hallucination control. Knows when to use which. | 20% | |
| Systems and scope judgment | Reaches for the simplest thing that works. Knows what to build vs buy vs skip. | 15% | |
| English fluency | Can lead a technical discussion with a US team async and live without friction. B2+ minimum. | 10% | |
| Autonomy under ambiguity | Operates when the spec is vague. Prioritizes well, ships, and asks the right questions early. | 5% |
How to use this: Score each criteria 1 to 5 across your interview panel. Multiply each score by the weight. Anyone above 3.8 weighted average is worth an offer. Anyone below 2.5 is a pass. The 2.5 to 3.8 range is where you make a judgment call based on how much of the gap is coachable.
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