WE ARE AI EXPERTS
Find the engineers that make your AI work.
Hire data annotators, AI/ML engineers and software developers from LATAM and save 58% in costs.
7d
First candidates delivered
1000+
Roles placed
58%
Cost savings vs. US hiring
18d
Average time to hire
How we work
From brief to hire in under 3 weeks.
Our process is simple: meet the Vintti AI team, have a call with one of our AI Recruiters to find the right technical and cultural match, and receive your first shortlist in just 7 days. Interview for free and only pay if you hire.
Step 01
Tell us what you need
Share your role, tech stack, and timeline. We handle the rest.
Day 1
Step 02
Call with your AI Recruiter
One of our AI Recruiters digs into the technical and cultural fit you need. This is where we get it right before we start sourcing.
Day 1 - 2
Step 03
We source & vet
Our team taps into a curated network of LATAM AI specialists. Every candidate is technically screened.
Day 2 - 7
Step 04
You meet your shortlist
3–5 pre-vetted profiles land in your inbox. Interview for free, pick your hire, and we handle compliance.
Day 7 - 18
Roles we place
The profiles your AI stack actually needs.
Not generic engineers. Specialists who have shipped real AI workflows for US companies, at LATAM rates.
Data Annotation Specialist
Data labeling, annotation, dataset curation, model evaluation
Prepares, structures, and labels the data that makes AI models actually work. Classifies unstructured datasets, builds fine-tuning datasets, and evaluates model outputs. The profile your AI team needs before the AI can do anything useful.
Also known as:
Data Labeler, ML Data Annotator, AI Training Data Specialist
What they do
- Labels and annotates text, image, and structured data following defined guidelines
- Classifies unstructured documents into usable categories
- QAs labeled datasets for consistency and accuracy
- Strong attention to detail and consistency under repetitive tasks
- English proficiency — many datasets require bilingual judgment
Tools
Salary: from
$
800
/ month
What they do
- Designs annotation schemas and labeling guidelines for specific ML projects
- Manages labeling workflows and ensures inter-annotator agreement
- Evaluates and scores LLM outputs for quality, safety, and alignment
- 2–4 years in data annotation or ML data operations
Tools
Salary: from
$
2000
/ month
What they do
- Owns end-to-end data pipeline: collection, labeling, QA, and delivery to ML teams
- Designs evaluation frameworks to measure model output quality at scale
- Runs red-teaming and adversarial testing on LLM outputs
- 4–7 years in ML data operations or AI training data roles
Tools
Salary: from
$
4000
/ month
Prompt Engineer
Prompt design, LLM evaluation, team enablement
Designs the prompts used by marketing, sales, and support teams. Builds prompt libraries, documents workflows, trains the team. For content-driven companies, this profile is worth its weight in gold.
What they do
- Writes and iterates on prompts for marketing, support, and sales teams
- Maintains a prompt library and documents best practices
- Tests outputs across different models and prompt variations
- Assists in training internal teams on how to use AI tools effectively
- Strong writing skills and linguistic sensitivity
- Daily hands-on experience with ChatGPT, Claude, or similar tools
Tools
Salary: from
$
1000
/ month
What they do
- Designs systematic prompt frameworks for multiple use cases across the business
- Runs structured evaluations (evals) to measure output quality
- Works with product and engineering to embed prompts in workflows
- 2–4 years in content strategy, UX writing, or AI-adjacent roles
Tools
Salary: from
$
1600
/ month
What they do
- Leads prompt architecture for product-level AI features
- Designs and runs rigorous eval pipelines to measure model quality at scale
- Works closely with ML engineers on fine-tuning and RLHF initiatives
- 4–7 years experience, including prompt engineering for production AI features
Tools
Salary: from
$
3500
/ month
AI/ML Engineer
Model training, fine-tuning, ML pipelines, production AI
Builds and fine-tunes ML models, designs training pipelines, and ships AI features into production. The profile that makes your models actually work at scale — data-in, insights-out, with the engineering rigor to back it up.
What they do
- Trains and fine-tunes ML models under senior guidance
- Prepares and cleans training datasets for model development
- Runs experiments and tracks results using MLflow or similar tools
- Assists in deploying models to staging environments
- 1–2 years of ML/AI experience or strong academic background
- Solid Python and familiarity with PyTorch or TensorFlow
- Basic understanding of ML concepts: loss functions, overfitting, evaluation
Tools
Salary: from
$
2000
/ month
What they do
- Fine-tunes pre-trained models (LLMs, vision, NLP) for specific use cases
- Designs and runs training pipelines end-to-end in cloud environments
- Evaluates model performance with rigorous metrics and test sets
- Collaborates with product teams to scope ML features for production
- 2–4 years in ML engineering or data science with production experience
- Strong Python, PyTorch or TensorFlow, and familiarity with HuggingFace
- Experience with training jobs on AWS, GCP, or Azure
Tools
Salary: from
$
3200
/ month
What they do
- Leads ML architecture decisions across multiple product lines
- Designs scalable training and inference infrastructure
- Owns model quality, reliability, and cost in production
- Mentors junior ML engineers and defines team best practices
- 4–7 years in ML engineering with production-grade model experience
- Deep expertise in fine-tuning LLMs, RLHF, and model evaluation at scale
Tools
Salary: from
$
5000
/ month
Evals Engineer
LLM evaluation, red-teaming, model quality at scale
Evaluates and stress-tests LLM outputs to ensure your AI product actually works in production. Knows how to design eval frameworks, run red-teaming, and measure model quality at scale. This is the profile every AI-native startup needs the moment they ship their first agent and the one most teams forget to hire until it's too late.
What they do
- Runs basic eval pipelines to measure LLM output quality
- Labels and scores model responses following defined rubrics
- Assists in building test datasets for regression and quality checks
- Documents failure modes and edge cases found during evaluation
Tools
Salary: from
$
3000
/ month
What they bring
- 1–2 years experience in QA, data annotation, or AI-adjacent roles
- Familiarity with Python and basic understanding of how LLMs work
- Strong attention to detail and systematic thinking
- Background in linguistics, cognitive science, or software testing is a plus
What they do
- Designs eval frameworks to measure accuracy, safety, and alignment of LLM outputs
- Runs red-teaming sessions to identify failure modes before production
- Builds automated eval pipelines integrated into the development workflow
- Collaborates with ML engineers and prompt engineers to improve model performance
Tools
Salary: from
$
5000
/ month
What they bring
- 2–4 years in QA engineering, ML data ops, or LLM-adjacent roles
- Solid Python — can write eval scripts and analyze results independently
- Understanding of RLHF, preference data, and model alignment concepts
- Experience with A/B testing or structured experimentation frameworks
What they do
- Owns the full eval strategy across all AI products and model versions
- Designs adversarial test suites and benchmark datasets from scratch
- Works closely with ML leadership to define quality standards and release criteria
- Builds internal tooling to automate and scale evaluation processes
Tools
Salary: from
$
8000
/ month
What they bring
- 4–7 years in ML evaluation, AI quality, or LLM engineering
- Deep understanding of model behavior, hallucination patterns, and mitigation strategies
- Experience shipping eval infrastructure used by engineering teams in production
- Able to translate model quality goals into concrete, measurable test cases
LLM Integration Developer
RAG, embeddings, LLM APIs, product AI features
Integrates GPT/Claude/Gemini directly into products. Knows RAG, embeddings, and APIs. This is the profile that replaces the $180k senior AI Engineer in the US, at a fraction of the cost.
What they do
- Integrates basic LLM APIs into existing applications under senior guidance
- Implements simple RAG pipelines with vector databases
- 1–2 years software development experience
- Solid Python and REST API knowledge
Tools
Salary: from
$
3500
/ month
What they do
- Builds production-ready RAG pipelines with chunking, retrieval, and reranking
- Integrates multiple LLM providers into product features
- Implements streaming, caching, and cost optimization strategies
- 2–4 years backend or ML engineering experience
Tools
Salary: from
$
6100
/ month
What they do
- Architects complex LLM systems with multi-agent orchestration
- Owns AI feature reliability, latency, and cost in production
- 4–7 years backend or ML engineering, with 2+ years on LLM systems
Tools
Salary: from
$
7100
/ month
Meet the talent ready to join your team.
A sample of pre-vetted profiles currently available. Every candidate is technically screened and US time-zone aligned.
Luciana M. — Data Annotation Specialist
Semi-senior
📍Santiago, CL
$3,200 / mo
Valentina R. — LLM Integration Developer
Semi-senior
📍Buenos Aires, AR
$3,500 / mo
Sebastião L. — Prompt Engineer
Semi-senior
📍São Paulo, BR
$2,300 / mo
These are just a few examples
Want to meet your future team member?
We'll match you with the right profile based on what you're building. Book a call and we'll show you who's available.
Why Vintti AI
Not a job board. Not a big agency.
A focused team obsessed with one thing: finding the exact AI profile your company needs, fast.
LATAM-first network
Deep roots in Argentina, Brazil, Colombia, and Mexico. Engineers who are US time-zone aligned and English fluent.
AI-specialized vetting
We test for real AI skills, not just Python fluency. Every candidate has shipped something that runs in production.
Fast. Actually fast.
First candidates in 7 days. Hire in under 3 weeks. Because your AI roadmap can't wait for a 3-month search.
60% cost savings
Senior AI talent at LATAM rates. No compromise on quality — the savings come from geography, not experience.
We stay in the loop
Onboarding support, 90-day check-ins, and a free replacement if something doesn't work out.
Compliance handled
Contracts, payroll, and legal coverage across LATAM countries. You hire, we handle the paperwork.
Pricing
Transparent pricing. No surprises.
Flexible plans that scale with you. You only pay when you hire, so the process is risk-free from day one.
Direct Recruiting
One-time fee
We find and vet the perfect match. You hire the candidate directly and manage them on your own.
✓
One-time payment — pay only if you hire
✓
You employ directly
✓
Interview for free, no upfront cost
✓
First candidates in 7 days
✓
AI-specialized vetting process
✓
LATAM talent pool, US time zones
✓
Laptop & equipment included (optional)
✓
Buyout option available
Staffing
Monthly contract
We become your full Employee-as-a-Service provider. Vintti AI hires, manages, and handles everything.
✓
Monthly fee (% of salary) + flat setup fee
✓
Payroll & compliance managed by us
✓
Laptop & equipment included (optional)
✓
Unlimited free replacements
✓
Buyout option anytime
Staff Augmentation
Project-based
You need to scale fast for a specific project. We assemble the right team from our LATAM network and have them ready in 18 days or less.
✓
Scale up or down based on project needs
✓
Pay only for hours worked
✓
Pre-vetted AI specialists, ready to deploy
✓
First team assembled in 18 days or less
✓
Payroll & compliance managed by us
Our talent has worked at:
What clients say
They stopped waiting. Their AI stack didn't.
★★★★★
"It's like walking into a store where someone actually understands what you need and guides you, instead of leaving you alone staring at a rack."
Head of Ops, E-commerce company, US
★★★★★
"True connection is your superpower. Your culture and your values — it's very clear that you guys are very different from other agencies."
CEO, Consumer brand, US
★★★★★
"We live in a day and age where customer service and care are really declining. I feel like Vintti AI is really bringing it back."
COO, Consumer brand, US
★★★★★
"It was personal, it was organized and it was professional. All together. I really haven't seen that combination anywhere else."
Founder, Marketing agency, US
★★★★★
"The level of competence in the candidates that Vintti AI brought to me were just a lot higher and outperformed the candidates from the other agencies."
CTO, AI-native startup, Canada
★★★★★
"The structure of the process is extremely clear. The team you have built is excellent. I want people like your team working for me."
CEO, Digital agency, US
About us
Our Story
We didn't build Vintti AI because we saw a market opportunity. We built it because we kept seeing the same thing: world-class engineers in LATAM — people building RAG systems, training LLMs, shipping production-grade agents — going completely unnoticed by the AI startups that needed them most.
Vintti AI was born inside Vintti, a LATAM-to-US staffing company that has placed hundreds of professionals across finance, operations, and tech. When AI started becoming central to how companies operate, we noticed a gap: demand for AI-specific talent was exploding, but no one in the staffing world actually understood those roles.
Everyone was placing "AI engineers." Nobody knew what that actually meant. We do. There's a difference between a Data Scientist, an LLM Integration Developer, a Prompt Engineer, and an AI Workflow Specialist. They're not the same profile. They don't cost the same. And you can't vet them the same way.
We don't want to be the biggest AI staffing agency, we want to be the one that actually gets it right. Our network is LATAM. Our specialty is AI. And our standard is: if we wouldn't hire them ourselves, we won't send them to you.
01
Small is intentional
We're not trying to scale into a 500-person agency. Small means every search gets our full attention.
02
AI-only, alway
We don't do generalist hiring. Every role we place lives at the intersection of talent and AI.
03
LATAM is our home
We know this talent market from the inside. Not from a database, but from relationships built over years.
04
Results over process
We don't care about how many candidates we send. We care about the one that actually works.
FAQs
Common questions.
Everything you need to know before you reach out.
How is Vintti AI different from a generalist staffing agency?
Most staffing agencies have an "AI division" they added in the last 18 months. Vintti AI was built specifically for AI talent from day one. That means we know the difference between the profiles we place, we know how to vet them technically, and we know which LATAM markets produce the best candidates for each role. A generalist agency sees a Python developer — we see whether that person can actually build a production RAG pipeline.
How fast can you actually deliver candidates?
First vetted profiles land in your inbox within 7 days of kick-off. Full hiring process — from first profile to signed offer — typically takes under 3 weeks for most roles. Senior or highly specialized positions may take up to 4 weeks. We'll tell you upfront if your role is likely to take longer.
Do I pay anything if I don't hire?
No. Zero. You can review profiles, run interviews, and decide not to move forward — and owe us nothing. We only charge when you hire. This is a deliberate choice: it keeps our incentives aligned with yours. If we don't find the right person, we haven't earned anything.
What happens if the hire doesn't work out?
It depends on your plan. On the Staffing model, replacements are unlimited and free — if it doesn't work out at any point, we find you someone else at no extra cost. On Direct Recruiting, we offer free replacements within the first 2 months of the hire. In all cases, we do proactive check-ins to catch any issues early.
Which countries in LATAM do you source from?
Primarily Argentina, Brazil, Colombia, and Mexico — where we have the deepest networks and the strongest AI talent density. We also place candidates from Chile, Uruguay, and Peru for select roles. All candidates are English-proficient and work US time zones.
How do you vet AI-specific skills?
It depends on the role. For LLM Integration Developers, we run technical challenges focused on RAG, embeddings, and API integration. For AI Automation Engineers, we assess their ability to build real workflows under realistic scenarios. For Prompt Engineers, we evaluate their systematic approach to prompt design and how they run evaluations. For AI Workflow Specialists, we look for real examples of transformation projects they've led. For Data Annotation Specialists, we test annotation consistency, labeling judgment, and their understanding of what makes a dataset actually usable for training.
What's the difference between your pricing plans?
Direct Recruiting is a one-time fee — you hire the person directly and manage them yourself. Staffing is a monthly model where Vintti AI acts as the employer of record: we handle payroll, compliance, equipment, and benefits, so you just work with your hire. AI Talent On-Demand is project-based — you need to scale fast, we assemble the right team from our LATAM network and have them ready in 18 days or less, and you pay only for hours worked. All three are risk-free: you only pay if you hire.
Vintti AI is built on 4 years of LATAM hiring.
Vintti has placed thousands of remote professionals in US and Canadian companies since 2022. When AI talent became the hardest hire in the market, the answer was obvious: go deeper. Vintti AI is what happens when a LATAM staffing company decides to specialize completely.
Your AI crew starts in LATAM.
Tell us what you're building and we'll find the engineer who can build it with you.