VINTTI AI · WE ARE AI EXPERTS
Pre-vetted LATAM AI experts — doctors, lawyers, engineers, finance pros and scientists — ready to evaluate and validate your AI models.
58%
average cost savings across all roles.
sTACK:
- Medical AI
- Legal AI
- Finance AI
- Science AI
- Engineering AI
- Robotics AI
Schedule your call
⏱ 30 min
Cost Comparison
By the numbers
The numbers that matter.
7d
Average time to first qualified candidates
68
%
Average cost savings vs US-based experts
6
+
Verticals covered by our talent pool
$0
Upfront cost — pay only when you hire
GET STARTED
Tell us what you need.
We’ll send you pre-vetted candidates in 7 days. You only pay if you hire.
Schedule your call
⏱ 30 min
No commitment. First candidates in 7 days. Pay only if you hire.
PROCESS
Let’s Connect
We get to know each other and make sure we're aligned on what you're looking for.
Takes 15 minutes
Let’s Learn Your Needs
We go deeper on the role: domain, annotation type (RLHF, evals, labeling), volume, and any required credentials. We handle qualification from there.
Takes 30 minutes
We Source & Vet
We screen for domain credentials, annotation experience, and English proficiency. You only see candidates that already passed our filter.
Day 7 onwards
You Hire, We Handle the Rest
Interview, select, and onboard. We manage contracts, payments, and compliance.
Hire in 18 days
COVERAGE
What domains can we staff for you?
Medical & Clinical AI
Physicians, radiologists, nurses, and pharmacists for medical imaging annotation, clinical NLP, diagnostic AI evaluation, and FDA-ready labeling pipelines.
- Medical Imaging
- Clinical NLP
- RLHF
- FDA Compliance
Legal & Compliance AI
Lawyers, paralegals, and compliance officers for contract analysis, legal reasoning evaluation, regulatory AI models, and due diligence automation.
- Contract AI
- Legal Reasoning
- Compliance
- Evals
Finance & Fintech AI
CPAs, financial analysts, and risk professionals for financial document AI, fraud detection models, investment tools, and regulatory reporting automation.
- Document AI
- Fraud Detection
- Risk Models
- FinReg
Science & Research AI
PhD scientists and researchers for scientific literature annotation, hypothesis generation models, lab data labeling, and research automation tools.
- Scientific NLP
- Lab Data
- Research AI
- Literature
Engineering & Technical AI
Civil, mechanical, electrical, and chemical engineers for technical documentation AI, CAD data labeling, industrial automation, and code evaluation.
- Code Evals
- Technical Docs
- Industrial AI
- CAD Data
Robotics & Automotive AI
Robotics engineers and automotive specialists for perception model annotation, autonomous systems evaluation, simulation data, and sensor fusion labeling.
- Perception AI
- Autonomous
- Simulation
- Sensor Fusion
WHY VINTTI AI
Vintti AI
Freelance Platforms
US-based Agencies
Technical assessment
Included and personalized
General workforce
Available, but costly
Time to first candidate
7 days
2–4 weeks setup
4–8 weeks
Cost vs US market
Up to 68% savings
Variable, low quality
Full US rates
Stack coverage
Pre-trained on RLHF
Generalist profiles
Depends on agency
Account management
Included 24/7
Self-serve only
Included, at a premium
Pay model
Pay only if you hire
Hourly + platform fees
Retainer or placement fee
WHAT THEY'LL DO FOR YOUR TEAM
Annotation services your new hires cover.
- RLHF Data Collection
- Model Output Evaluation
- AI Evals & Benchmarking
- Domain-specific Data Labeling
- Fine-tuning Dataset Curation
- Red-teaming & Safety Testing
- Instruction-following Datasets
- Human Preference Ranking
- Medical Image Annotation
- Legal Document Classification
- Financial Report Labeling
- Scientific Literature Tagging
- Code Review & Eval
- NLP Dataset Creation
- Synthetic Data Validation
- QA for Foundation Models
Roles we place
Find other roles for your AI stack needs.
Not generic engineers. Specialists who have shipped real AI workflows for US companies, at LATAM rates.
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
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
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
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
NO COMMITMENT REQUIRED
Great AI starts with the right people.
Tell us the role, stack and seniority you need. We send pre-vetted candidates in 7 days. You only pay if you hire.