Hire Pre-Vetted AI/ML
Engineers From Latin America
Our AI/ML engineers average 4+ years of experience across PyTorch, TensorFlow, and production model deployment. Every candidate clears a technical screening, a live coding exercise, and an English fluency check. They ship production code from week one.
Happy Customers Hiring Latin American AI/ML Talent
Most In-Demand AI/ML Roles
The six roles B2B SaaS teams request most often. Start here, or browse the full role directory below.
Machine Learning Engineer
Generalist engineers building and training production ML models.
Explore Role →LLM Specialist
RAG, fine-tuning, and inference specialists working with OpenAI and Anthropic models.
Explore Role →AI/ML Engineer
PyTorch and TensorFlow engineers shipping models to production.
Explore Role →Generative AI Engineer
Engineers building generative applications on top of foundation models.
Explore Role →Data Scientist
Analysts turning raw data into models and product-ready insights.
Explore Role →MLOps Engineer
Pipeline engineers automating model deployment, monitoring, and rollback.
Explore Role →What AI/ML Engineers Do In B2B SaaS Companies
Model Development & Training
Builds, trains, and fine-tunes machine learning models using PyTorch or TensorFlow, from data preprocessing through evaluation.
LLM & Generative AI Integration
Integrates large language models into products through RAG pipelines, fine-tuning, and prompt engineering, using tools like LangChain and Hugging Face.
MLOps & Production Deployment
Owns the pipeline from trained model to production endpoint, handling versioning, monitoring, and rollback with Kubeflow or MLflow.
Data Infrastructure & Evaluation
Manages the data pipelines, feature stores, and evaluation frameworks that keep models accurate and safe as usage scales.
Cost Savings: Hire AI/ML Engineers In Latam Vs. USA
Hiring AI/ML engineers from Latin America gets you the same caliber of model development, MLOps, and LLM integration talent at roughly 30% under US rates, with enough timezone overlap for daily standups.
Based on the top 50% of salaries across generalist, LLM specialist, and infrastructure-focused AI/ML roles in the region. Top 10% earners (AI infrastructure architects, staff ML engineers) typically command higher rates.
Our Process To Recruit & Hire AI/ML Talent In 21 Days
Kickoff & Search
Sign the agreement, pay the retainer, and recruitment begins. Our Talent Partners headhunt 1,100 to 1,700 qualified AI/ML candidates matching your model stack, language, and timezone requirements.
Screening & Evaluation
Candidates go through technical screening, live coding exercises, English fluency tests, and reference checks. We're checking for real production experience, not resume keywords.
Selection & Onboarding
You review the top candidates and choose who joins your team. We handle legal agreements, payroll onboarding, and IP transfer so your new engineer is ready to ship in 21 days.
Explore Every AI/ML Role We Place
LatamCent places pre-vetted, full-time AI and machine learning engineers across every major discipline. They're grouped below so you can find the exact role your team needs.
Core ML & Software Engineering
Generalist engineers who build, train, and ship machine learning models to production.
Generative AI & Language
Specialists working with large language models, from fine-tuning to production prompts.
Speech, Vision & Biometrics
Engineers working with audio, image, and identity recognition models.
MLOps, Infrastructure & DevOps
The engineers and managers who keep models deployed, monitored, and scaling reliably.
AI Strategy, Architecture & Safety
The roles that decide what to build, keep it secure, and keep it accountable.
Automation, Product & Documentation
Roles that connect AI systems to real workflows, users, and documentation.
LatamCent makes nearshoring feel like an
extension of AI & B2B SaaS teams
Before LatamCent, we spent hours sorting through resumes that didn’t fit.
We needed SDRs who could start fast and deliver results right away.
We needed to scale our engineering and delivery teams fast without sacrificing quality.
LatamCent helped us grow our customer base while staying on budget.
We needed to expand our analytics team fast but still stay within budget.
We needed someone to step in fast and keep our marketing momentum strong.
LatamCent made hiring engineers simple.
I always say there’s no Steve Jobs without Steve Wozniak, and that’s what this hire became for us.
LatamCent helped us scale quickly when growth was creating demand our team couldn’t handle.
Hiring CSMs in the U.S. wasn’t scalable, but LatamCent made it possible.
We had reached a point where contractors were slowing us down instead of helping us scale.
As our engineering team in Brazil grew, finding developers with the right experience became difficult.
