About the role
The ML Engineer role consists of training, fine-tuning, and deploying custom models — classification, recommendation, forecasting, vision — when a foundation model API isn't the right tool. If the value is in your proprietary data and the problem is predictive rather than generative, this is the role. Most companies need an AI Engineer first; the ones with real data moats need this too.
Monthly rate
$5,500–$8,000/mo
All-in: contract, benefits, equipment, IP
Experience
10+ years engineering
5+ in ML production
Location
Latin America
Argentina · Colombia · Mexico
Timezone
Full US overlap
Fluent English, onboarded in one week
Core stack
AI tools, daily
Verticals seen
What they own — and what they don't
What they own
- Frame business problems as ML problems — and say when ML is the wrong tool
- Train and fine-tune models on your data: recommendation, scoring, forecasting, vision, NLP
- Own the training pipeline: feature engineering, experiment tracking, evaluation methodology
- Deploy models behind real APIs with monitoring for drift and degradation
- Decide fine-tune vs prompt vs classic ML per problem, on cost and accuracy evidence
What they don't — and who does instead
- Build product features on top of foundation-model APIs — that's an AI Engineer
- Run the serving/GPU platform at scale — that's MLOps
- Build the data pipelines that feed training — that's a Data Engineer
- Do exploratory analysis for business decisions — that's a Data Scientist
Who hires this role, and for what
Companies with proprietary data and a prediction problem. Churn, fraud, demand, pricing — when the answer lives in years of their own data, an API call to a foundation model won't find it.
Products where recommendations drive revenue. Marketplaces, media, e-commerce: a few points of recommendation quality move real money, and that's custom-model territory.
Teams whose LLM costs outgrew the use case. Some high-volume tasks running on frontier models are cheaper and faster as a small fine-tuned model. ML Engineers make that trade.
- 01
Predictive models on business data. Churn, fraud, credit scoring, demand forecasting — trained on your history, deployed behind your API.
- 02
Recommendation systems. What to show next, to whom — the classic revenue-moving ML problem.
- 03
Fine-tuning for volume tasks. Distilling a high-volume LLM task into a small custom model that's 10x cheaper per call.
- 04
Vision and document models. OCR pipelines, quality inspection, medical imaging — where the model has to be yours.
Work our engineers at this role have shipped
- Real-time fraud scoring model for a Series C fintech — sub-100ms serving at 500K daily events
- Ranking model for a marketplace, replacing rule-based sort with a learned re-ranker
- Demand forecasting for a LATAM retail chain, replacing spreadsheet planning
Do you actually need an ML Engineer?
You do, if:
- The problem is prediction on your own data, not generation of text
- You've validated with a heuristic or an LLM and hit its accuracy or cost ceiling
- Recommendation or scoring quality is directly tied to revenue
- You have labeled data (or a plan to get it) and nobody who can exploit it
You probably don't, if:
- The use case is 'chat with our docs' or text generation — an AI Engineer with RAG solves that faster and cheaper
- You have no data yet — instrument first, hire when there's something to learn from
- You need one model deployed once — consider a scoped project before a full-time hire
Not sure which role fits? Tell us the problem instead of the title — we'll tell you what we'd actually staff, even if it's not this. If it is this: discovery call today, matched profiles in 48 hours, onboarded in a week.
Hire a Senior ML Engineer