Hire AI Developers From Latin America
Add full-time AI engineers to your team, from LLM and generative AI specialists to MLOps and machine learning engineers, without US salaries or a six-month search. Every candidate is technically vetted and fluent in English, and you make the hire in 21 days or less.
- Popular:
- AI agents
- RAG & search
- MLOps
- Chatbots
- Computer vision
LLM & RAG
Works US hours
- 4+ yrsaverage AI/ML experience
- 10 daysto your first 3 to 5 profiles
- 21 daysor less to make the hire
- 1,100+candidates headhunted per search
Companies building their teams with LatamCent
Find the AI Developer Your Team Needs
Search by role, skill or tool, or start from what you're building. Add roles to the team builder to compare what the team costs in Latin America and in the US.
No exact match for that search. We also run searches for AI roles that aren't listed here.
Describe the role you needCore ML & AI engineering 7 roles
Machine Learning Engineer
Most requestedBuilds, trains and ships production ML models, from feature work to evaluation.
AI Software Engineer
Most requestedBuilds LLM features into your product: APIs, streaming, evals and token cost control.
Data Scientist
Most requestedTurns product and customer data into models, experiments and decisions.
Designs and trains neural networks for vision, language and multimodal problems.
Builds forecasting, churn and scoring models that feed product and revenue teams.
Trains systems that learn from feedback, from recommendation policies to RLHF.
Builds systems that reason over unstructured data with knowledge graphs and semantic search.
Generative AI & language 5 roles
Generative AI Engineer
Most requestedBuilds generative features on foundation models across text, image and multimodal.
LLM Specialist
Most requestedRAG, fine-tuning and inference specialists for commercial and open-source models.
Designs, tests and versions the prompts and context behind your AI features.
Classification, extraction and search over text, with classic NLP and LLMs.
Builds chatbots and voice assistants that handle real customer conversations.
Vision, speech & biometrics 5 roles
Detection, segmentation, OCR and document AI for images and video.
Speech-to-text pipelines for calls, meetings and voice products.
Natural voice synthesis for assistants, IVR and content.
Face, voice and fingerprint recognition with liveness checks and privacy built in.
Runs models on devices and gateways when latency, cost or privacy rule out the cloud.
MLOps & AI infrastructure 5 roles
MLOps Engineer
Most requestedOwns the path from trained model to production endpoint: versioning, monitoring and rollback.
Leads the ML platform team and sets the standards for how models ship.
Builds AI workloads on AWS, Google Cloud and Azure managed services.
Tunes GPU clusters and distributed training so large models train faster and cheaper.
Designs the compute, data and serving stack your AI products run on.
Architecture, quality & safety 3 roles
Decides which models, data and integrations fit the product, then designs the system.
Tests AI features for accuracy, regressions and edge cases before customers find them.
Builds the guardrails, bias testing and audit trails that keep AI accountable.
Automation, product & docs 4 roles
Combines LLMs, bots and APIs to automate back-office workflows end to end.
Builds software bots for repetitive, rules-based work across your systems.
Turns AI capability into product: scoping, evals, pricing and launch.
Writes the docs, API references and model cards that developers and buyers rely on.
Need an engineer who deploys AI inside your customers' environments? We also hire forward deployed engineers.
What AI/ML Engineers Do in B2B SaaS Companies
Most SaaS teams need some mix of these four jobs. Knowing which one is your bottleneck tells you which role to hire first.
LLM & generative AI features
Builds copilots, agents and RAG search into the product with LangChain, LlamaIndex and the OpenAI or Anthropic APIs, with evals to prove they work.
Model development & training
Builds, trains and fine-tunes models in PyTorch or TensorFlow, from data preparation through evaluation.
MLOps & production deployment
Owns the path from trained model to production endpoint, with versioning, monitoring and rollback in Kubeflow, MLflow or SageMaker.
Data infrastructure & evaluation
Runs the data pipelines, feature stores and evaluation frameworks that keep models accurate and safe as usage grows.
AI Developer vs ML Engineer vs Data Scientist
The titles get used loosely. Here's how the four most-requested roles actually split the work.
| Compare | AI developer | ML engineer | Data scientist | MLOps engineer |
|---|---|---|---|---|
| Main job | Builds AI features into your product on top of existing models | Builds, trains and ships custom models | Analyzes data and runs experiments to decide what to build | Gets models into production and keeps them running |
| Ships | Copilots, agents, RAG search, AI-powered workflows | Ranking, recommendation, forecasting and classification models | Analyses, experiments, prototypes and metrics | Pipelines, deployment, monitoring and rollback |
| Core tools | LLM APIs, LangChain, vector databases, your app stack | PyTorch, TensorFlow, scikit-learn, feature stores | Python, SQL, notebooks, statistics | MLflow, Kubeflow, SageMaker, Docker |
| Hire first when | You want AI features in the product this quarter and good models already exist | Off-the-shelf models don't fit and your data is the edge | You need to find where AI would pay off before you build | You have models that break, drift or can't ship |
Why US Teams Hire AI Developers in Latin America
Model work moves in short loops: try an approach, check the evals, talk it through, try again. Those loops slow to a crawl when half the team is asleep.
- Feedback the same dayAn eval run that finishes at 2pm in New York gets discussed at 2pm, not the next morning.
- A senior bench outside the bidding warExperienced ML and LLM engineers who aren't fielding five Bay Area offers a week, at salaries that leave room in the budget.
- Engineers who stayFull-time hires build context in your data and codebase over months, which is where most AI quality comes from.
Hiring in a specific country? Our Latin America hiring guides cover each market.
- Mexico City8 of 9 hrs
- Bogotá9 of 9 hrs
- Santiago7 of 9 hrs
- Buenos Aires7 of 9 hrs
- São Paulo7 of 9 hrs
Assumes each engineer works a local 9am to 6pm day. Overlap shifts by about an hour when US daylight saving time starts.
AI Developer Salaries: Latin America vs the US
An AI/ML engineer in Latin America averages around $54K a year, compared with roughly $135K for equivalent US talent.
What that looks like for a small AI pod
An AI developer, an ML engineer and an MLOps engineer: roughly $405K a year in US salaries, or about $162K in Latin America. Build your own mix in the team builder.
Averages are based on the top 50% of salaries across generalist, LLM specialist and infrastructure-focused AI/ML roles in the region. Top earners such as AI infrastructure architects and staff ML engineers command higher rates. Weighing a visa hire instead? Compare both with our H-1B vs nearshore ROI calculator.
How to Hire an AI Developer: What to Screen For
What to look for, what to ask and what should worry you, whether you hire through us or on your own.
Skills worth testing
Strong AI developers are strong engineers first.
- Production experience. Has shipped a model or AI feature that real users touch, not only notebooks and demos.
- Evaluation habits. Measures quality with test sets, evals or A/B tests, and can show you the numbers.
- LLM fluency. RAG, function calling, context design, and cost and latency trade-offs.
- Core ML. Data preparation, feature work, training, and knowing when a simpler model wins.
- Software engineering. Clean Python, APIs, tests, version control and CI. AI code is still code.
- Clear communication. Explains trade-offs to product and sales in plain English.
Red flags in interviews
Patterns we screen out before candidates reach you.
- Talks about tools and frameworks, never about results or metrics.
- Every project is a tutorial, course capstone or demo with no users.
- Can't explain why a model failed or what they changed afterwards.
- Treats prompts as magic: no tests, no versioning, no fallback.
- Reaches for a bigger model before checking the data or retrieval.
Six interview questions that separate real AI engineers
Each one comes with what a strong answer covers.
- Walk me through an AI feature you shipped. How did you know it worked?Listen for: real users, a metric that moved, and the eval set or test behind it.
- Our LLM feature gives confident wrong answers. Where do you start?Listen for: retrieval quality, grounding, failure cases turned into evals and guardrails, not just a bigger model.
- When would you fine-tune instead of using RAG or better prompts?Listen for: fine-tuning for format, style or narrow tasks with good data; RAG for fresh or private knowledge; the maintenance cost of each.
- How would you cut the cost of this AI feature in half?Listen for: caching, smaller models for easy calls, batching, shorter context, and checking quality after each change.
- A model did well offline but underperforms in production. What happened?Listen for: data drift, training-serving skew, leakage, delayed labels, and the monitoring that would catch it.
- Explain this design to our head of sales in two minutes.Listen for: no jargon, a focus on what changes for the customer, and honest limits.
From Kickoff to an AI Hire in 21 Days
Each search is run by Talent Partners who recruit AI and ML engineers every week, so they know what a strong RAG or MLOps profile looks like.
Kickoff & search
Sign the agreement, pay the retainer and the search starts. Talent Partners headhunt 1,100 to 1,700 AI/ML candidates matching your model stack, language and time zone needs.
Screening & evaluation
Candidates go through technical screening, a live coding exercise, an English fluency test and reference checks. Your first 3 to 5 vetted profiles usually land within 10 days.
Selection & onboarding
You interview the shortlist and pick who joins. We handle legal agreements, payroll onboarding and IP transfer, so your new engineer starts shipping right away.
Two Ways to Bring AI Engineers Onboard
Pick the model that fits how you run your team. The search, vetting and timeline are the same either way.
| Option | Talent as a Service | Direct Hire |
|---|---|---|
| Who employs the engineer | LatamCent, as contractor of record | Your company |
| Contracts, payroll and compliance | Handled by us in the engineer's country | Handled by you |
| If the hire isn't right | We replace them, with no limit on replacements | Covered by a replacement guarantee |
| Good fit for | Growing an AI team fast without setting up entities abroad | Companies that already employ people in Latin America |
LatamCent makes nearshoring feel like an
extension of your AI team
I was blown away because LatamCent was able to find more than one qualified candidate who met all of it. It turned out it wasn’t a unicorn, at least not for you.
LatamCent made hiring engineers simple.
We needed to expand our analytics team fast but still stay within budget.
We needed to scale our engineering and delivery teams fast without sacrificing quality.
Hiring AI Developers From Latin America
Have a question about your own search? Send it to our team and a Talent Partner will reply.
Tell us what you're building, the model stack and the seniority you need. Our Talent Partners headhunt candidates across the region, and each one clears a technical screening, a live coding exercise, an English fluency check and reference checks before you see them. You interview a shortlist and make the hire within 21 days.
An AI/ML engineer in Latin America averages around $54K a year, compared with roughly $135K for equivalent US talent, a saving of about $81K per hire. Specialists such as AI infrastructure architects and staff ML engineers command higher salaries, while generalist and QA roles sit closer to the average.
LatamCent places engineers who build AI agents and copilots, usually from three roles: LLM specialists, generative AI engineers and AI software engineers. Tell us which tools the agent needs to use and how it will be evaluated, and we screen for that work specifically.
Freelancers suit a short, well-defined project. If the AI work is part of your product, a full-time engineer who learns your data, codebase and customers ships more and stays accountable. LatamCent places full-time engineers who work as part of your team.
We place machine learning engineers, AI software engineers, LLM and generative AI specialists, NLP and conversational AI engineers, computer vision and speech engineers, MLOps engineers and managers, AI architects, AI QA and ethics engineers, automation engineers, and AI product and documentation roles. Use the role finder on this page to see each one.
Most roles are filled in 21 days or less. Talent Partners usually deliver the first 3 to 5 vetted profiles within 10 days, and interviews and final selection wrap up over the following one to two weeks.
Yes. Our engineers live in Latin American time zones that line up with the US workday, so they join your standups, code reviews and customer calls in real time.
We place four levels: junior (1 to 2 years, support and prompt-level tasks), mid-level (3 to 5 years, owns model pipelines end to end), senior (6 to 8 years, leads architecture decisions and mentors), and director-level (9+ years, defines AI strategy).
An AI developer builds AI features into a product, often on top of existing models. A machine learning engineer builds, trains and ships the models themselves. A data scientist analyzes data and runs experiments to decide what to build. Smaller teams often start with one person who covers two of the three.
Candidates work with PyTorch, TensorFlow and Hugging Face for model development, LangChain and LlamaIndex for LLM applications, and Kubeflow, MLflow and AWS SageMaker for deployment and monitoring. We match the shortlist to your stack.
Yes. We regularly staff AI pods, pairing an ML engineer or solutions architect with MLOps, data and QA specialists. Use the team builder on this page to sketch the team and send it to us.
We run the search again and find a replacement. Talent as a Service has no limit on replacements, and Direct Hire placements come with a replacement guarantee.
Tell Us About Your AI Team
Share who you need and a Talent Partner will confirm the profile with you, then start the search. First vetted profiles usually arrive within about 10 days.
No roles picked yet. Add roles in the role finder above, or just describe what you need in the form.
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