Anyone can wire up an API call to an LLM. We place senior full-stack AI engineers who ship agents to production: tool use, retrieval and memory, evals, tracing, cost control, and the product around the model.
Agents, RAG and LLM features that handle real traffic, with evals, tracing and cost control, not just a demo.
One engineer who builds the model integration, the backend and the UI around it. No handoff gaps.
“AI engineer” is a crowded title. We screen out prompt-tinkerers and keep the people who’ve actually shipped.
AI / Full-Stack Engineers
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.
3-5 people who fit the exact role, not a résumé dump.
We screen AI engineers on a real task, an agent or RAG feature plus an eval, and an architecture interview.
Not a fit? We replace at no cost.
Operators who’ve made this hire, not account managers.
We start with a 30-minute call. You tell me the role, the stack, and what “great” looks like. No charge, no obligation.
We screen against the role-specific bar and send 3-5 people in days, not a résumé dump.
Meet your favorites. We coordinate everything. Most clients hire within ~3 weeks of the first call.
We handle contracts and payroll via Talent Management (COR). Not a fit in 90 days? We replace at no cost.
AI engineering hiring is where the most demo-vs-production gap shows up. Four traps founders fall into.
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.
Any AI engineer worth hiring builds evals and observability from day one. If a candidate cannot explain how they'd measure whether their AI feature is getting better or worse, they haven't shipped to real users.
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.
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.
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.
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.
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.
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.
Ship. We specifically screen out demo-only candidates, our test includes evals, monitoring and cost considerations.
Yes. Tool use, multi-step planning, memory and retrieval, guardrails, evals and tracing. We screen on a real agent or RAG build, so you see how they handle failure cases before you hire.
Yes. The major model APIs, LangChain / LlamaIndex or plain SDKs, pgvector or Pinecone, memory layers, and tracing tools. They join your repo and follow your standards from day one.
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.
Mid-to-senior. People who’ve owned features end-to-end and can make architecture calls without hand-holding.
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.
Senior AI and ML engineers in Latin America run $80,000 to $110,000 per year base, roughly 50 percent below equivalent US senior rates of $180,000 to $240,000. Full stack generalists are closer to $66K to $102K. See the Compensation Report for country specifics.
Awana is operator-led and screens AI engineers on a real RAG build plus an architecture interview, not a prompt quiz. 500+ placements, 98 percent placement success, 3-month replacement guarantee, 5-day shortlist. Comparison against Revelo, Howdy, BairesDev and others in our Best LatAm Recruiting Agencies breakdown.
“Send me your agent roadmap. I’ll send you engineers who’ve already shipped something like it, to production, not just a demo.” Calvin, CEO of Awana