Hire the best AI Engineers in Latin America
400k+
ENGINEERS
14 days
to hire
100+
COVERED
30-50%
US hires
Why hire AI Engineers through Revelo?
Rigorously vetted senior developers from Latin America who work in your timezone, ready to contribute from day one.
Interview only the best AI Engineers
A shortlist of three to five pre-vetted candidates, hand-picked by in-market recruiters. You decide who to interview, you decide who to hire.
One platform for talent, payroll, taxes and compliance
Your team runs legally across 18 countries in Latin America. Manage your engineers without managing the infrastructure underneath them.
Local recruiting experts invested in your hire
In-market recruiters and account managers cover sourcing, offer strategy, and onboarding. They stay with you until your engineer is up and running.
Your team, your terms
Month-to-month engagements mean you're never locked into headcount you don't need. Scale up for a big push, pull back after launch.
Hire the top 1% of AI Engineers in Latin America
Hire vetted senior developers, matched to your stack, your timezone, and your budget.
Hire AI Engineers who can deliver this and more
Here's what you get when you hire nearshore AI Engineers with Revelo.
Hire AI EngineersRevelo's AI developers build the systems that bring large language models and AI capabilities into production applications. Companies hire them to turn AI from a demo into a reliable product feature.
RAG Pipeline Development
Revelo engineers design chunking strategies, embedding pipelines, and retrieval logic that ground LLM responses in your company's actual data, producing accurate, source-cited answers instead of hallucinations.
LLM Integration and Prompt Engineering
Revelo engineers integrate OpenAI, Anthropic, or open-source models into your application with well-structured prompts, caching, and fallback logic, building LLM layers that are reliable, cost-controlled, and easy to iterate on as models improve.
AI Feature Prototyping
Revelo engineers build functional proofs of concept that validate whether an AI approach actually works for your use case before you commit to a full build, moving from idea to working prototype in days.
Evaluation and Guardrails
Revelo engineers implement evaluation frameworks that measure AI output quality systematically, plus guardrails that prevent harmful or off-topic responses, giving you the testing infrastructure to ship AI features with confidence.
Vector Search Implementation
Revelo engineers set up and optimize vector databases like Pinecone, Weaviate, or pgvector for semantic search, recommendations, and similarity matching, handling embedding model selection, indexing strategies, and hybrid search that combines vector and keyword results.
Looking for related expertise? Check out Revelo's AI/ML developers, AI product developers, and Python developers for machine learning and backend AI work.
Hire AI Engineers in 4 simple steps
Get from "we need someone" to your first day together in weeks, not months.
Tell us what you're building and what kind of AI Engineers you need: skills, experience level, team dynamics.
Three to five matched, pre-vetted candidates: identity-checked, skills-tested, human-screened. No wading through hundreds of profiles.
Run your own technical interviews. You decide who to interview and who to hire. Full control, no gatekeeping.
Make the offer. Revelo handles payroll, benefits, taxes, and compliance so you can focus on building. Your engineer ships code from day one.
10+ years making it easier to hire elite nearshore AI Engineers
Interview pre-vetted candidates who are fluent in English and work in your timezone.
Start hiring
Why hire AI Engineers based in Latin America?
Work synchronously with AI Engineers in the same or overlapping US time zones. Real-time collaboration, no async tax.
What are AI Engineers?
An AI developer integrates artificial intelligence into production applications, connecting pre-trained models, APIs, and retrieval systems into software that end users actually interact with. This is one of the fastest-growing engineering roles since 2023, driven by large language models and the gap between what models can do in a demo and what they need to do reliably in production.
Day-to-day, AI developers build RAG pipelines that ground LLM responses in company data, design prompt chains and structured outputs, manage vector databases for semantic search, handle model evaluation and monitoring, and optimize for the latency-cost-quality tradeoffs that define real AI products. The work is mostly about making models useful and reliable inside existing systems.
What separates a strong AI developer is production judgment: they've shipped AI features that handle edge cases gracefully, built evaluation frameworks that catch hallucinations before users do, and know when to call an API versus when to hand off to an ML team for a custom solution.
Why hire AI Engineers?
AI features have moved from impressive demos to baseline business expectations. Your customers want intelligent search, smart recommendations, and natural language interfaces, and they expect those features to work reliably in production. Building them requires a specific kind of engineer who understands prompt engineering, retrieval, evaluation, and observability across the full integration stack.
The talent gap is real and widening. AI developers who can take a prototype from notebook to production are in extremely short supply. The field is only a few years old in its current form, so experience is best measured by shipped products.
Revelo gives you access to 400,000+ pre-vetted engineers based in Latin America, with a shortlist in 72 hours and average time to hire of 14 days. Revelo's AI developers work in your timezone, understand the full integration stack, and bring 30–50% cost savings compared to equivalent US hiring.
What does it cost to hire AI Engineers?
US AI developer salaries run well into six figures at every seniority level. Junior AI developers start notably higher than most software engineering entry points, while senior AI developers command a meaningful premium over senior generalist engineers, with top-quartile earners pushing well above $200,000 in total employer cost.
AI developers based in Latin America through Revelo cost significantly less than their US counterparts. Per Revelo's 2025 Salary Guide, senior AI/ML engineers from Brazil and Argentina run $143,000–$204,000 all-in per year; mid-level engineers start lower. These figures cover engineer compensation, benefits, compliance, and Revelo's management fee in a single monthly rate. Visit revelo.com/pricing for current role-specific figures.
| Seniority | US Total Employer Cost (est.) | Revelo All-In Monthly Rate |
|---|---|---|
| Junior | ~$120,000/yr | Meaningfully below US junior rates |
| Mid-Level | ~$160,000/yr | Well into six figures annually |
| Senior | ~$230,000/yr | ~$11,900–$17,000/mo |
Revelo's all-in monthly rate includes payroll, benefits, compliance, and account management. No placement fee, no hidden markup.
Why hire in Latin America?
Latin America has built genuine depth in artificial intelligence research and applied engineering. Brazil's top universities (USP, Unicamp, and UFRJ) run established AI research labs, and Argentina's UBA has produced influential work in machine learning. A growing AI startup scene across São Paulo, Buenos Aires, and Mexico City means AI developers are moving between research and production, building the applied skills that US companies need most.
AI engineering involves rapid iteration: prompt tuning, model evaluation, pipeline debugging. That work moves fastest when your team shares working hours. A LatAm AI developer online during US business hours means experiment results get discussed immediately, with major hubs sitting within 0–2 hours of US Eastern time.
AI work requires constant communication about tradeoffs between accuracy, latency, and cost, blending engineering decisions with product thinking. LatAm AI developers who've built alongside US teams navigate those conversations in fluent English with the context those discussions demand.
How to evaluate AI Engineers
Start with retrieval. Ask candidates to design a RAG pipeline from scratch: how they chunk documents, which embedding model they pick, and how they decide between vector search and hybrid retrieval. Strong answers discuss chunk overlap, metadata filtering, and why retrieval quality is the primary lever for reducing hallucination in production. Weak answers describe the tools without discussing the decisions.
Then move to prompt engineering and evaluation. How do they structure prompts for consistency across varied inputs? Ask them to walk through how they'd build an eval suite, what metrics they track beyond intuition, and how they catch regressions when the underlying model gets updated. The strongest candidates version prompts the way engineers version code.
For senior roles, probe cost-quality tradeoffs and production hardening. How do they choose between a large frontier model and a smaller fine-tuned one for a given task? Ask about latency budgets, caching strategies, guardrails for harmful output, and how they handle failures when an API provider goes down mid-request. A senior AI developer should have opinions built from production experience.
Why expertise matters
Why AI Engineering Wins for Intelligent Products
Companies that staff dedicated AI engineering see measurable gains across three dimensions: faster feature velocity (prototypes ship in days rather than quarters because engineers own the full integration stack), measurable quality improvement through systematic evaluation frameworks that catch regressions before users do, and cost-controlled AI at scale through caching, model selection, and latency optimization that keeps inference costs from compounding as usage grows. That combination of speed, quality, and cost discipline is what separates teams that ship reliable AI features from teams that stay stuck in demo mode.
Common Use Cases
Conversational interfaces, semantic search, document summarization, code generation, content recommendations, and automated workflows are where dedicated AI engineering delivers the most visible returns. Each represents a point where a pre-trained model must be integrated, evaluated, and maintained inside a real product.
AI Engineering Spans Every Industry
Dedicated AI engineering is no longer limited to AI-native companies. Established players in fintech, edtech, SaaS, and enterprise software now staff AI engineers alongside their core product teams, integrating retrieval, summarization, and generation into products that predate the LLM era.
When AI Is the Wrong Choice
Not every problem benefits from AI. If a rules-based or algorithmic approach already solves the problem cleanly, the added complexity and inference cost work against you. Reserve AI for the cases where deterministic logic genuinely falls short.
Benefits of working with AI Engineers
Revelo's AI developers build the systems that bring large language models and AI capabilities into production applications. Companies hire them to turn AI from a demo into a reliable product feature.
RAG Pipeline Development
Revelo engineers design chunking strategies, embedding pipelines, and retrieval logic that ground LLM responses in your company's actual data, producing accurate, source-cited answers instead of hallucinations.
LLM Integration and Prompt Engineering
Revelo engineers integrate OpenAI, Anthropic, or open-source models into your application with well-structured prompts, caching, and fallback logic, building LLM layers that are reliable, cost-controlled, and easy to iterate on as models improve.
AI Feature Prototyping
Revelo engineers build functional proofs of concept that validate whether an AI approach actually works for your use case before you commit to a full build, moving from idea to working prototype in days.
Evaluation and Guardrails
Revelo engineers implement evaluation frameworks that measure AI output quality systematically, plus guardrails that prevent harmful or off-topic responses, giving you the testing infrastructure to ship AI features with confidence.
Vector Search Implementation
Revelo engineers set up and optimize vector databases like Pinecone, Weaviate, or pgvector for semantic search, recommendations, and similarity matching, handling embedding model selection, indexing strategies, and hybrid search that combines vector and keyword results.
Looking for related expertise? Check out Revelo's AI/ML developers, AI product developers, and Python developers for machine learning and backend AI work.
What Is an AI Developer?
An AI developer integrates artificial intelligence into production applications, connecting pre-trained models, APIs, and retrieval systems into software that end users actually interact with. This is one of the fastest-growing engineering roles since 2023, driven by large language models and the gap between what models can do in a demo and what they need to do reliably in production.
Day-to-day, AI developers build RAG pipelines that ground LLM responses in company data, design prompt chains and structured outputs, manage vector databases for semantic search, handle model evaluation and monitoring, and optimize for the latency-cost-quality tradeoffs that define real AI products. The work is mostly about making models useful and reliable inside existing systems.
What separates a strong AI developer is production judgment: they've shipped AI features that handle edge cases gracefully, built evaluation frameworks that catch hallucinations before users do, and know when to call an API versus when to hand off to an ML team for a custom solution.
Why Hire AI Developers?
AI features have moved from impressive demos to baseline business expectations. Your customers want intelligent search, smart recommendations, and natural language interfaces, and they expect those features to work reliably in production. Building them requires a specific kind of engineer who understands prompt engineering, retrieval, evaluation, and observability across the full integration stack.
The talent gap is real and widening. AI developers who can take a prototype from notebook to production are in extremely short supply. The field is only a few years old in its current form, so experience is best measured by shipped products.
Revelo gives you access to 400,000+ pre-vetted engineers based in Latin America, with a shortlist in 72 hours and average time to hire of 14 days. Revelo's AI developers work in your timezone, understand the full integration stack, and bring 30–50% cost savings compared to equivalent US hiring.
What Does It Cost to Hire an AI Developer?
US AI developer salaries run well into six figures at every seniority level. Junior AI developers start notably higher than most software engineering entry points, while senior AI developers command a meaningful premium over senior generalist engineers, with top-quartile earners pushing well above $200,000 in total employer cost.
AI developers based in Latin America through Revelo cost significantly less than their US counterparts. Per Revelo's 2025 Salary Guide, senior AI/ML engineers from Brazil and Argentina run $143,000–$204,000 all-in per year; mid-level engineers start lower. These figures cover engineer compensation, benefits, compliance, and Revelo's management fee in a single monthly rate. Visit revelo.com/pricing for current role-specific figures.
| Seniority | US Total Employer Cost (est.) | Revelo All-In Monthly Rate |
|---|---|---|
| Junior | ~$120,000/yr | Meaningfully below US junior rates |
| Mid-Level | ~$160,000/yr | Well into six figures annually |
| Senior | ~$230,000/yr | ~$11,900–$17,000/mo |
Revelo's all-in monthly rate includes payroll, benefits, compliance, and account management. No placement fee, no hidden markup.
Why Hire AI Developers in Latin America?
Latin America has built genuine depth in artificial intelligence research and applied engineering. Brazil's top universities (USP, Unicamp, and UFRJ) run established AI research labs, and Argentina's UBA has produced influential work in machine learning. A growing AI startup scene across São Paulo, Buenos Aires, and Mexico City means AI developers are moving between research and production, building the applied skills that US companies need most.
AI engineering involves rapid iteration: prompt tuning, model evaluation, pipeline debugging. That work moves fastest when your team shares working hours. A LatAm AI developer online during US business hours means experiment results get discussed immediately, with major hubs sitting within 0–2 hours of US Eastern time.
AI work requires constant communication about tradeoffs between accuracy, latency, and cost, blending engineering decisions with product thinking. LatAm AI developers who've built alongside US teams navigate those conversations in fluent English with the context those discussions demand.
How to Evaluate AI Candidates
Start with retrieval. Ask candidates to design a RAG pipeline from scratch: how they chunk documents, which embedding model they pick, and how they decide between vector search and hybrid retrieval. Strong answers discuss chunk overlap, metadata filtering, and why retrieval quality is the primary lever for reducing hallucination in production. Weak answers describe the tools without discussing the decisions.
Then move to prompt engineering and evaluation. How do they structure prompts for consistency across varied inputs? Ask them to walk through how they'd build an eval suite, what metrics they track beyond intuition, and how they catch regressions when the underlying model gets updated. The strongest candidates version prompts the way engineers version code.
For senior roles, probe cost-quality tradeoffs and production hardening. How do they choose between a large frontier model and a smaller fine-tuned one for a given task? Ask about latency budgets, caching strategies, guardrails for harmful output, and how they handle failures when an API provider goes down mid-request. A senior AI developer should have opinions built from production experience.
Why AI Expertise Matters
The role fits products adding intelligent features: conversational interfaces, semantic search, document summarization, code generation, content recommendations, and automated workflows. The common thread is taking a pre-trained model and integrating it into a product with proper guardrails, latency budgets, cost controls, and evaluation frameworks.
As of 2026, OpenAI, Anthropic, Google, Microsoft, Notion, Duolingo, and Stripe all employ dedicated AI engineering teams building production features (per public engineering blogs and verified production deployments). Notion's AI assistant and Duolingo's AI tutor are two visible examples of what AI engineering produces at consumer scale.
One caveat: if your problem has a clean deterministic solution (rules, formulas, standard algorithms), adding AI introduces unnecessary complexity, cost, and unpredictability. AI also requires data. Without training data, user feedback loops, or evaluation datasets to measure quality, you'll ship a feature you can't improve. Start with the simplest solution that works, then layer in AI where it earns its place.
How Revelo Vets AI Developers
Every developer in Revelo's network passes a rigorous multi-stage screening process before being made available to clients. Only the top 2% of applicants make it through, which is why 73.1% of Revelo's actual placements are senior engineers.
The process starts with recruiter-led pre-screening of professional experience, skills, and written communication. Next comes an English fluency assessment, written and verbal, because clear communication matters as much as clean code when working across time zones.
Then comes the technical deep dive. For AI developer candidates, that means hands-on evaluation of model selection, prompt engineering, RAG architectures, and production ML deployment. Revelo tests problem-solving and code quality.
Candidates also complete a hands-on skill challenge and soft-skills evaluation covering real-world problem-solving, async collaboration, and remote-work readiness, followed by a live interview with a senior technical reviewer who pressure-tests depth and fit.
Revelo stays involved after placement with ongoing check-ins, so any friction surfaces early, before it slows your team down.
Benefits of Building With AI
Why AI Engineering Wins for Intelligent Products
Companies that staff dedicated AI engineering see measurable gains across three dimensions: faster feature velocity (prototypes ship in days rather than quarters because engineers own the full integration stack), measurable quality improvement through systematic evaluation frameworks that catch regressions before users do, and cost-controlled AI at scale through caching, model selection, and latency optimization that keeps inference costs from compounding as usage grows. That combination of speed, quality, and cost discipline is what separates teams that ship reliable AI features from teams that stay stuck in demo mode.
Common Use Cases
Conversational interfaces, semantic search, document summarization, code generation, content recommendations, and automated workflows are where dedicated AI engineering delivers the most visible returns. Each represents a point where a pre-trained model must be integrated, evaluated, and maintained inside a real product.
AI Engineering Spans Every Industry
Dedicated AI engineering is no longer limited to AI-native companies. Established players in fintech, edtech, SaaS, and enterprise software now staff AI engineers alongside their core product teams, integrating retrieval, summarization, and generation into products that predate the LLM era.
When AI Is the Wrong Choice
Not every problem benefits from AI. If a rules-based or algorithmic approach already solves the problem cleanly, the added complexity and inference cost work against you. Reserve AI for the cases where deterministic logic genuinely falls short.
Frequently asked questions
Everything you need to know about hiring AI Engineers through Revelo.
How much does it cost to hire AI Engineers through Revelo?
All-in monthly costs run roughly $4,600–$5,600 for junior, $5,800–$7,500 for mid-level, and $7,200–$10,700 for senior developers: engineer compensation, PEO coverage, and Revelo's margin combined. No placement fee, no surprise invoices.
How quickly can I hire AI Engineers through Revelo?
You'll see a curated shortlist of matched, pre-vetted candidates within 72 hours, and most companies make a hire within 14 days of sharing their requirements.
What is Revelo's vetting process for AI Engineers?
Every candidate is identity-checked, skills-tested, and human-screened: technical assessments matched to their stack, soft-skills and English-fluency interviews, and review by in-market recruiting experts before they ever reach your shortlist.
What engagement models does Revelo offer for AI Engineers?
Month-to-month, full-time engagements, with no long-term lock-in. Scale up for a big push or pull back after launch as your roadmap evolves, with a 14-day risk-free trial on every hire.
What happens after I hire AI Engineers through Revelo?
Revelo handles payroll, benefits, taxes, and compliance across 18 countries, and your dedicated account manager stays with you through onboarding and beyond. Your engineer ships code from day one.
How quickly can I hire an AI developer through Revelo?
Most clients receive a shortlist of pre-vetted AI developer candidates within 72 hours. Interviews happen the following week, and onboarding can start as soon as you make an offer. Average time to hire runs under two weeks from initial call to the engineer building production AI features on your stack.
What does it cost to hire an AI developer through Revelo?
Rates vary by seniority. Junior AI developers come in meaningfully below US junior rates, while lead engineers scale above $14,000 per month. All rates are all-in: payroll, benefits, compliance, and account management are included in one monthly figure, with no placement fee added on top. See current figures at revelo.com/pricing.
What is Revelo's vetting process for AI developers?
Revelo runs every candidate through a multi-stage assessment before matching them with any client: AI and applied ML skills testing, live coding, English fluency evaluation, and soft-skills screening. Only the top 2% of applicants make it through, which is why the vast majority of Revelo's placed AI developers are senior engineers.
What engagement models does Revelo offer?
Full-time dedicated is the standard model: the developer works exclusively for your team, reports to your managers, and joins your standups. All engagements run month-to-month with no long-term contract and no cancellation penalty, so you can scale the team up or down as your roadmap shifts.
What happens if the match doesn't work out?
Revelo offers a 14-day risk-free trial. If the fit isn't right within those first two weeks, Revelo replaces the developer at no cost to you. Revelo's account management team stays involved through the onboarding period to surface and resolve any collaboration friction early. To get a shortlist of vetted AI developers in 72 hours, visit Revelo.
Our AI Engineers know these tech stacks and more
Our talent is experienced in these libraries, APIs, platforms, frameworks, and databases.
Ready to hire AI Engineers?
See a curated shortlist of pre-vetted candidates in 72 hours. Only pay if you hire.