Hire a Top Generative AI Engineer. Same Quality. Up to 73% Less.

Latin America is the best place to hire an offshore Generative AI Engineer for most U.S. companies, because engineers in Brazil, Argentina, Mexico and Colombia are shipping production LLM applications today and work U.S. hours. Mid-level engineers there cost $4,000 to $6,500 a month, salaries across levels run 59 to 73% below U.S. equivalents, and USD contracts are standard.

Time-zone overlap matters more for this role than for most engineering work, because generative AI quality improves through fast loops: change a prompt or retrieval setting, rerun the eval set, review the failures with your product team and ship the fix the same day. Colombia (UTC-5), Mexico City (UTC-6), São Paulo and Buenos Aires (UTC-3) all sit within 2 hours of U.S. Eastern time, so your engineer joins standups and live debugging sessions in real time.

The tradeoff is cost. The Philippines is the budget option, from $1,500 a month for junior engineers to $6,000 for senior, and fits API-integration work such as adding a chatbot or RAG search to an existing product; Manila runs 12 to 13 hours ahead of U.S. Eastern time, so it suits well-scoped work with less live iteration. South Africa, from $2,100 to $9,000 a month, brings strong software-engineering fundamentals to LLM application and eval pipeline work, with overlap on the U.S. East Coast morning.

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An offshore Generative AI Engineer builds the LLM features your product and operations run on: retrieval-augmented generation (RAG) pipelines over your documents, fine-tuned and prompt-tuned models, AI agents that call your APIs, and the evaluation suites that keep answers accurate. A full-time mid-level engineer in Latin America costs $4,000 to $6,500 a month, versus $14,000 to $18,000 for a U.S. hire, about 67% less.

That cost gap matters because most generative AI projects stall after the demo. A chatbot that answers 10 test questions well still needs retrieval tuning, guardrails, controls on token spend, logging and an eval set of 100 or more real queries before it is safe to ship. A dedicated engineer owns that work 40 hours a week, so the pilot your team approved reaches production and stays accurate as models and your data change.

Somewhere sources and screens candidates for your brief, including hands-on work with the model providers, vector databases and frameworks you name, and you interview the finalists before making an offer. At the senior level, a Latin American engineer costs $6,500 to $11,000 a month, which saves up to about $153,000 a year against a senior U.S. hire at $19,000 to $24,000.

The main benefit of hiring an offshore Generative AI Engineer is cost per shipped feature: one full-time engineer at $4,000 to $6,500 a month in Latin America covers the retrieval, prompt, agent and evaluation work a U.S. hire at $14,000 to $18,000 a month would. Here are the five benefits U.S. companies weigh most when they hire AI engineers offshore.

Up to 73% lower cost: A junior engineer in Latin America costs $2,500 to $3,800 a month versus $10,000 to $13,000 in the U.S., and a senior hire saves up to about $153,000 a year.

Full-time ownership of your LLM features: One engineer working 40 hours a week carries each use case from prototype to retrieval tuning, guardrails, deployment and monitoring, so features keep improving after launch.

Same-day iteration in U.S. hours: Engineers in Colombia, Mexico, Brazil and Argentina work within 2 hours of U.S. Eastern time, so prompt changes, eval results and fixes land the same day.

Model choices based on your own data: An engineer who has built on OpenAI, Anthropic and open-source models can test 2 or 3 options against your eval set, so you pick a model on accuracy and cost per request.

Hiring support focused on fit. Somewhere sources and screens candidates for your brief. Interview the finalists and agree on responsibilities, working hours and expectations before making an offer.

Hiring an offshore Generative AI Engineer through Somewhere takes five steps, and most clients review a shortlist within about 5 business days of sharing their brief. Every placement is full-time and dedicated to your team, with salaries for this role starting at $1,500 a month in the Philippines and $2,500 in Latin America.

1. Share your needs. In a call of about 30 minutes, outline the use case (RAG search over internal documents, a customer-facing assistant, AI agents or fine-tuning), your model providers (OpenAI, Anthropic or open-source models), your data and cloud stack, and the seniority you need, from junior at $2,500 a month to senior at up to $11,000 in Latin America.
2. Compare hiring options. Direct Hire uses a one-time recruiting fee based on the hire’s first-year salary. Talent On-Demand uses monthly pricing. Compare service options and request a quote for your role.
3. Review your shortlist. Receive 3 to 5 screened candidates, each checked for shipped LLM features, retrieval and evaluation experience, Python or TypeScript depth, English communication and the tools in your brief.
4. Interview the finalists. Hold 1 to 2 interviews per candidate. A practical prompt works well for this role: ask each finalist how they would cut wrong answers from a RAG assistant by half in 30 days, and which metrics they would track.
5. Hire and onboard. Confirm the offer and prepare your new team member’s access, tools and responsibilities. With Direct Hire, your company manages onboarding, payroll, compliance and day-to-day work.

Plan the first 90 days before the start date: name 1 or 2 use cases to ship first, the data sources and model access the engineer needs, a starting eval set of 50 to 100 real questions, and the metric you will use to judge success, such as answer accuracy or support tickets resolved without a human. A written 90-day scope shortens the path from a signed offer to a first LLM feature in production.