VINTTI AI · WE ARE AI EXPERTS
Pre-vetted LATAM ML engineers — Python, PyTorch, HuggingFace & MLflow experts — ready to build and deploy AI models with average savings of 62% vs US hiring costs.
58%
average cost savings across all roles.
sTACK:
- LLM Development
- Model Training
- MLOps
- Data Pipelines
- Model Deployment
- Computer Vision
Schedule your call
⏱ 30 min
Cost Comparison
By the numbers
The numbers that matter.
7d
Average time to first qualified candidates
62
%
Average cost savings vs US-based experts
8
+
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: PyTorch vs TensorFlow, MLOps vs research, LLM fine-tuning vs CV pipelines, team size, and any hard requirements. We own the technical qualification from there.
Takes 30 minutes
We Source & Vet
We screen for stack depth, system design skills, and production experience. You only see engineers who passed our technical bar — coding assessment, architecture review, and English proficiency included.
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 can your LATAM AI/ML Engineers build?
LLM Development & Fine-tuning
Engineers who fine-tune open-source models (Llama, Mistral, Falcon), build RAG pipelines, and deploy production-ready LLM applications using HuggingFace and OpenAI APIs.
- Fine-tuning
- RAG
- HuggingFace
- RLHF
Computer Vision
Specialists in object detection, image segmentation, and video analytics using PyTorch and OpenCV. Build perception systems, medical imaging models, and industrial inspection AI.
- PyTorch
- OpenCV
- Object Detection
- Segmentation
MLOps & Model Deployment
Engineers who own the full ML lifecycle — training pipelines, experiment tracking with MLflow, model serving, monitoring, and CI/CD on AWS SageMaker or GCP Vertex.
- MLflow
- SageMaker
- Vertex AI
- CI/CD
Data Science & Predictive Modeling
Analysts and engineers who build forecasting models, recommendation systems, churn prediction, and anomaly detection using scikit-learn, XGBoost, and Pandas.
- Scikit-learn
- XGBoost
- Pandas
- Forecasting
NLP & Text AI
Engineers building document classification, sentiment analysis, named entity recognition, and summarization systems using transformer models and spaCy.
- Transformers
- spaCy
- NER
- Text Classification
Research & Experimentation
ML researchers who design and run experiments, benchmark model performance, contribute to novel architectures, and publish reproducible results using Jupyter and W&B.
- W&B
- Jupyter
- Benchmarking
- Research
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 62% savings
Variable, low quality
Full US rates
Stack coverage
PyTorch, HuggingFace, MLflow, AWS
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
Tools and frameworks your new hires work with
- Python
- PyTorch
- TensorFlow
- HuggingFace Transformers
- MLflow
- AWS SageMaker
- GCP Vertex AI
- Scikit-learn
- Pandas / NumPy
- Jupyter Notebook
- Weights & Biases
- OpenCV
- FastAPI
- Docker / Kubernetes
- SQL
- Matplotlib / Seaborn
- LangChain
- Pinecone / Weaviate
- JavaScript / TypeScript
- Git / GitHub Actions
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
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
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
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.