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Hire AI Engineers Who Ship, Not Just Prototype

Anyone can wire up an API call to an LLM. We place senior full-stack AI engineers who ship production GenAI, RAG pipelines, agents, evals, and the product around them.

Get in Touch → See the stack we screen for

Why hire a full-stack AI engineer

Ship AI to production

RAG, agents and LLM features that handle real traffic, with evals, monitoring and cost control, not just a demo.

Full-stack ownership

One engineer who builds the model integration, the backend and the UI around it. No handoff gaps.

Cut through the noise

“AI engineer” is a crowded title. We screen out prompt-tinkerers and keep the people who’ve actually shipped.

What a full-stack AI engineer does

Build LLM, RAG and agent features full-cycle

Design prompts and the evaluation loops that keep them honest

Integrate vector databases and model APIs

Build the Next.js / React frontend and Node.js / Python backend around the AI

Ship to production with monitoring, cost and latency control

Optimize quality, cost and speed as usage grows

We test for the bar, not the buzzwords

Python. ML/AI tooling, model APIs, LangChain / LlamaIndex, eval scripts.

TypeScript & Node.js. Typed application backends and API layers around the model.

React & Next.js. Production AI product UIs, streaming, RSC, the works.

RAG architecture. Vector DBs (pgvector, Pinecone), retrieval, evals and observability.

System design. Designing AI features that stay reliable and affordable at scale.

Why hire AI engineers from Latin America

LatAm’s AI and ML community is one of the fastest-growing in the world, with engineers who’ve shipped GenAI in production, and full timezone overlap means you can actually pair with them while they build, not wait for tomorrow’s commit.

Same hours0-3h US overlap. Live standups, not async lag.

Real EnglishNear-native, low-ego, direct collaboration.

50% lower costSenior talent where living costs less.

Proven quality98% of placements stay past the guarantee.

The risk is on us, not you

Pre-vetted shortlists

3-5 people who fit the exact role, not a résumé dump.

Tested on a real build

We screen AI engineers on a real task, a RAG feature plus an eval, and an architecture interview.

98% + 3-month guarantee

Not a fit? We replace at no cost.

Founder-led

Operators who’ve made this hire, not account managers.

From first call to hired in ~3 weeks

Discovery call

We start with a 30-minute call. You tell me the role, the stack, and what “great” looks like. No charge, no obligation.

Pre-vetted shortlist

We screen against the role-specific bar and send 3-5 people in days, not a résumé dump.

You interview & choose

Meet your favorites. We coordinate everything. Most clients hire within ~3 weeks of the first call.

Onboard + guarantee

We handle contracts and payroll via Talent Management (COR). Not a fit in 90 days? We replace at no cost.

Common mistakes when hiring AI engineers in LatAm

AI engineering hiring is where the most demo-vs-production gap shows up. Four traps founders fall into.

Hiring for prompt engineering, not systems engineering

Prompt engineering is a skill inside a role, not a role. Great AI engineers ship RAG pipelines, evaluate them, own the whole stack around the model. Filter for engineers who've shipped AI to production users, not just to a demo.

Ignoring evaluation and observability

Any AI engineer worth hiring builds evals and observability from day one. If a candidate can't explain how they'd measure whether their AI feature is getting better or worse, they haven't shipped to real users.

Underweighting product sense

AI features are product features. Engineers who treat them as pure ML problems ship the wrong thing. The best AI engineers we place have opinions about UX, latency, and error states, not just about model choice.

Not scoping infrastructure early

Model choice is the smallest cost. Compute, vector DB, observability, and evaluation infra all add up. Great AI engineers can architect this from day one. Junior engineers pass the cost to you six months in.

Hiring AI engineers, your questions

What’s the difference between an AI engineer and an ML engineer?

An ML engineer trains and deploys models; an AI engineer builds products on top of existing models (LLMs, APIs), RAG, agents, features. Most startups need the latter to ship.

AI engineer vs AI evaluator, what’s the difference?

Engineers build the AI product; evaluators judge model output quality. If you need humans to rate and red-team output, see hire AI evaluators in Latin America.

Can your AI engineers work full-stack?

Yes, that’s the point. They build the model integration, the backend and the frontend, so you don’t need three hires to ship one feature.

What AI / LLM stacks do they know?

Python with LangChain / LlamaIndex, vector DBs like pgvector and Pinecone, the major model APIs, plus TypeScript, Node.js, React and Next.js for the product layer.

Can they ship to production, or just prototype?

Ship. We specifically screen out demo-only candidates, our test includes evals, monitoring and cost considerations.

Should I hire an AI engineer or use your AI Development service?

Hire when you want a dedicated engineer on your team long-term; use our AI Development service when you want us to build it with you. We’ll help you decide on the call.

How senior are these engineers?

Mid-to-senior. People who’ve owned features full-cycle and can make architecture calls without hand-holding.

Can you hire a Solana or Web3 developer in Latin America?

Yes. Web3 and Solana talent is a specialty pool in Latin America, mostly concentrated in Argentina, Brazil, and Colombia, where crypto-native startups like Ripio, Buenbit and Bitso trained a generation of senior blockchain engineers. We place Solana, Ethereum, and full-stack Web3 developers as part of the AI/full-stack engineer track.

What does a senior AI engineer in LatAm cost?

Typically 50% below US rates for equivalent seniority. See the Compensation Report or get in touch for specifics.