AIProduction AI hiring · LATAM · US working-hour overlap

Hire Nearshore AI Engineers Who Can Ship Production AI

Hire senior LATAM engineers for RAG systems, AI agents, LLM product features, model infrastructure, and AI workflow automation. They work with US working-hour overlap, join your repo, and are vetted by engineers for real production work, not AI buzzwords on a resume.

Get matched with AI engineers →See how we vet them

✓ Interview matched candidates within 72 hours
✓ Senior LATAM engineers with US working-hour overlap
✓ Vetted for RAG, agents, evals, code quality, and production debugging
✓ 14-day trial with replacement or refund if the fit is wrong

Trusted talent for teams at

Production judgment

AI hiring is full of inflated resumes

Everyone has OpenAI, LangChain, RAG, agents, and fine-tuning on their profile now. That does not mean they can ship AI inside a real product.

We look for engineers who can explain tradeoffs, debug bad retrieval, design evals, control hallucinations, handle latency, protect user data, and work inside an existing codebase without turning your product into an experiment.

That is the difference between an AI demo and production AI.

Role specificity

Not all AI engineers do the same job

The strongest hire depends on whether you need product LLM work, custom models, retrieval, or agent workflows. Use these specialty pages to narrow the role before you interview.

Hire LLM engineers

For RAG, agents, prompt reliability, evals, and LLM product features.

Hire generative AI engineers

Hire ML engineers

For proprietary models, training pipelines, MLOps, drift monitoring, and experimentation.

Hire machine learning engineers

Hire RAG engineers

For grounded AI search over documents and internal knowledge.

Hire RAG engineers

Hire agentic AI engineers

For tool-using workflows, approvals, state, retries, and observability.

Hire agentic AI engineers

AI-assisted engineering

Do your engineers use AI coding tools?

Yes. The engineers we place are expected to work with modern coding tools like Claude Code, Codex, and Copilot when those tools help them move faster. The standard is not tool usage by itself; it is whether the engineer can turn generated code into reliable production software.

Human review stays in the loop

Generated code still goes through PR review, architecture judgment, and the client's normal review standards.

Tests and evals are part of the workflow

For AI features, we look for engineers who pair code generation with tests, evals, regression checks, and clear failure-mode thinking.

No blind trust in generated code

Good AI engineers know when Claude Code, Codex, or Copilot are useful, and when the output needs to be rewritten, constrained, or rejected.

Start with the work

What are you trying to build?

You do not need to diagnose the exact job title before talking to us. Start with the product problem, and we will map it to the right AI engineer. We care about production judgment before keyword matching.

RAG and AI search

For teams that need reliable AI over internal docs, support content, legal documents, product data, patient records, or knowledge bases.

PineconeWeaviatepgvectorrerankingRAG evals

Hire RAG engineers ->

AI agents and workflow automation

For teams that want AI to take action across tools, handle retries, respect approvals, and stay observable instead of just answering questions.

LangGraphtool usestateretriesobservability

Hire agentic AI engineers ->

AI Forward Deployed Engineers

For teams that need embedded engineers who can discover client workflows, customize AI systems, manage stakeholders, and preserve vendor optionality.

discoveryagentsRAGevalsrollout

Hire AI FDEs ->

LLM product features

For SaaS teams adding copilots, assistants, summarization, structured outputs, classification, or user-facing AI workflows.

OpenAIClaudeLlamastructured outputsguardrails

Hire LLM engineers ->

Model fine-tuning and optimization

For teams that need lower inference cost, stronger domain performance, open-weight deployment, or GPU-aware model serving.

LoRAQLoRAvLLMquantizationGPU efficiency

MLOps and AI infrastructure

For teams moving from prototype to production with evals, monitoring, CI, model serving, data pipelines, and drift checks.

MLflowSageMakerVertex AICI/CDmonitoring

Computer vision and multimodal AI

For OCR, document understanding, inspection, media tooling, visual QA, and multimodal workflows that need production reliability.

PyTorchOpenCVOCRYOLOmultimodal

Tell us what you are building →

Vetting framework

How We Vet Nearshore AI Engineers

AI resumes are getting inflated fast. Everyone can list RAG, agents, and LLMs. Our vetting focuses on production judgment: what they shipped, how they debug, how they reason about failure, and whether they can work inside your existing engineering process.

We look for shipped production AI — not Kaggle notebooks. Strong coursework is not enough. We want engineers who can talk through retrieval failures, eval design, latency, broken tool calls, security concerns, and messy user behavior.

Step 01

Production Portfolio Review

We look for shipped production AI — not Kaggle notebooks. We want to see real product work, messy constraints, user behavior, and systems that survived contact with production.

Step 02

AI Failure-Mode Assessment

We test judgment around bad retrieval, hallucinations, broken tool calls, latency, eval failures, data privacy, and non-deterministic debugging.

Step 03

Live Coding & Design Session

We watch how they reason through a realistic product problem, open tradeoffs, handle edge cases, and explain what they would ship first.

Step 04

Communication & Time-Zone Sync

We confirm fluent business English, async-writing skill, and meaningful US working-hour overlap for standups, pairing, planning, and code review.

Step 05

Background & Reference Check

We confirm their track record with real references — past tech leads, not friends — and look for the habits that make remote engineering work.

Step 06

Onboarding & Ongoing Compliance

We handle payroll, IP assignment, NDAs, and local-labor-law compliance in LATAM, then keep a close feedback loop after placement.

Role clarity

Not sure which AI role you need?

The titles overlap. We help you map the work to the right profile so you do not overhire, underhire, or interview the wrong kind of specialist.

For: adding AI to product

AI Engineer

A product-minded engineer who can add AI features to an existing app without turning your codebase into an experiment.

Best fit: you need practical AI features inside a SaaS or internal product.

For: LLM-heavy products

LLM Engineer

A specialist in GPT, Claude, Llama, RAG, structured outputs, tools, prompt design, and evals.

Best fit: your product depends heavily on LLM behavior and reliability.

For: AI over knowledge bases

RAG Engineer

A retrieval specialist who builds grounded AI over documents, vector databases, hybrid search, reranking, citations, and RAG evals.

Best fit: the main risk is whether your AI finds and cites the right source material.

For: proprietary data

ML Engineer

A specialist in custom models, training pipelines, model serving, data workflows, and production ML operations.

Best fit: you have proprietary data and need models beyond API orchestration.

For: small teams

Full-Stack AI Engineer

An AI engineer who can also build the surrounding product: UI, auth, APIs, payments, infrastructure, and integrations.

Best fit: an early-stage team needs one person who can ship the AI feature and the product around it.

For: workflow automation

AI Agent Engineer

A specialist in multi-step workflows, tool use, state, retries, approvals, observability, and failure recovery.

Best fit: the work involves AI taking action across systems, not just generating text.

2026 benchmarks

The Real Cost of Hiring a Senior AI Engineer in 2026

Direct comparison: a senior AI engineer hired in-house in the US, sourced through a freelance marketplace in LATAM, or placed through us. The savings come from regional compensation differences, not junior talent arbitrage.

In-house US

Freelance LATAM

Next Idea Tech

Annual base salary

High senior AI comp

Lower regional rates

Managed LATAM engagement

Total comp (incl. equity, bonus)

Equity, bonus, and benefits add materially

Client negotiates directly

Predictable monthly cost, no equity dilution

Contract, benefits & payroll burden

Handled by internal HR and finance

Client manages contract, compliance, and payments

Managed engagement, EOR, payroll, and benefits support

Recruiter / placement fees

Often paid separately

Marketplace fee varies

Included in engagement

Time to first interview

3–4 weeks

1–2 weeks

72 hours

Time to signed engagement

8–12 weeks

4–6 weeks

Often under 14 days

AI-specific vetting

You run it

Self-reported

Engineer-led production AI vetting

IP assignment & NDA

HR / counsel

Client manages paperwork

Handled before work starts

Risk-free trial period

—

14-day replacement or refund window

Lower

All-in cost than US senior AI hiring

72 hr

Time to your first qualified interview

14 days

Trial period, fully risk-free

Day-one integration

Built to Fit Into Your Engineering Workflow

Nearshore staff augmentation means the engineer joins your team. Your repos. Your Jira. Your Slack. Your eval harness, or we help define one if you do not have it yet. Your CI pipeline. Your PR review process. From day one, they work inside your repo, open PRs under your review process, and follow your CI, security, and deployment standards.

✓Provisioned in your SSO, MDM, and access controls
✓Works under your data-handling and security model (SOC2-aligned)
✓Integrated into your evals (LangSmith, Braintrust, Inspect)
✓Daily standups, async writing, code review at your tempo

Brief us on your stack →

GitHub

Jira

Slack

Linear

Notion

Vercel

AWS

Datadog

Lang
Smith

LangSmith

Pine
cone

Pinecone

Weav
iate

Weaviate

ML
flow

MLflow

Zero-risk AI hiring

14-Day Risk-Free Trial

Try the engineer for 14 days. If the fit is wrong, we replace them or refund the trial period. No long-term contract required.

The point is simple: you should see how they work in your repo, with your team, before committing to a long engagement.

Get matched with AI engineers →

14 days

Replacement or refund window

✓Replacement or refund if the fit is wrong during the trial
✓We recalibrate the search if the role changes
✓No long contract required before you validate fit

Trusted by US AI & SaaS teams

Real engineers. Real teams. Real reviews.

Verified feedback from US founders and CTOs we've placed AI and full-stack engineers with.

They built guardrails, payments, and UX faster than I could explain the next idea.

Next Idea Tech turned my hacked-together prototype into something investors and customers actually trust. They owned the UX, dev, and infra like an in-house team.

Leo F.

Founder, Radar

Their professionalism and the timeliness of delivery most impressed us.

Next Idea Tech guided an efficient process to deliver valuable insight that supports our ML roadmap. The team communicated effectively and shipped on every milestone.

John C.

CTO, The Peak Beyond

They are always communicative and keep us abreast of any obstacles.

Despite the complexity of our agent orchestration work, the team has been able to follow deadlines and coordinate well even with multiple stakeholders across our org.

Courtney S.

CEO, Officer Reports

What you're hiring for

What does a senior AI engineer do in 2026?

A senior AI engineer in 2026 doesn't just train models. They design and ship production systems that use LLMs, vector databases, fine-tuned open-weight models, and autonomous agents to solve real business problems.

On a typical week, an AI engineer we place might:

Build a retrieval-augmented generation (RAG) pipeline over a client's proprietary documents, using Pinecone or Weaviate as the vector store.
Fine-tune a small open-weight model (Llama, Mistral, Qwen) on domain-specific data using LoRA or QLoRA.
Design eval harnesses that catch regressions in non-deterministic LLM outputs before they reach production.
Ship a multi-step agent (LangGraph, CrewAI, or Claude's native tool use) that automates a workflow previously done by humans.
Optimize inference cost by quantizing models with GPTQ or AWQ and serving them through vLLM or TGI.
Build guardrails and red-team prompts against prompt injection, jailbreaks, and PII leakage.

The toolkit moves fast. The engineers we place stay current — and we re-vet them annually against the current frontier of the field.

Frequently asked

FAQs About Hiring Nearshore AI Engineers

If you don't see your question here, ask in the brief form below — we'll answer.

How quickly can we meet matched AI engineers?

Most teams can review matched profiles and interview qualified nearshore AI engineers within 72 hours. The faster path is a clear brief on your product, stack, AI workflow, seniority level, and any security constraints.

Can we hire a nearshore AI engineer for a short-term project?

Yes. Staff augmentation works well for short-term AI projects when the engineer joins your tools, repos, and review process. We still handle IP assignment, NDAs, payroll, and local compliance so the engagement does not become a contractor-management burden.

What should a US startup look for in a nearshore AI engineering partner?

Look for engineer-led vetting, production AI proof, strong English, US working-hour overlap, clear IP assignment, and a replacement process if the fit is wrong. For AI roles, generic coding tests are not enough; you need evidence that the engineer can debug retrieval, agents, evals, latency, and messy product behavior.

How do you vet AI skills beyond resume keywords?

We care less about whether a profile lists OpenAI, LangChain, RAG, or agents, and more about whether the engineer can explain tradeoffs. We look at shipped work, system design, failure modes, eval strategy, security judgment, code quality, and how they communicate under ambiguity.

How long does it take to hire a nearshore AI engineer?

You can usually interview matched engineers within 72 hours. A signed engagement often happens in under 14 days when the role, stack, budget, and security requirements are clear.

How do you protect IP and handle compliance when hiring nearshore AI engineers?

We handle confidentiality agreements, IP assignment, payroll, taxes, benefits, and local labor compliance before the engineer starts. For regulated teams, we align access, data handling, and documentation with your security process.

Can a nearshore AI engineer integrate with our existing codebase, evals, and CI?

Yes. The engineer works inside your repo, opens PRs under your review process, follows your CI and deployment standards, and joins your planning rhythm. If you do not have an eval harness yet, we can help define the first version with you.

What's the cost difference between hiring an AI engineer in the US versus nearshore in LATAM?

Nearshore hiring can lower all-in cost because of regional compensation differences, not because the work should be junior or low-quality. The right comparison is total cost, seniority, time-zone overlap, compliance, replacement support, and whether the engineer has shipped production systems.

Our story

Built by Engineers Who've Hired Engineers

Our philosophy

"We vet for more than code. We look for thinkers, not just doers. Good hiring is about people, not just pipelines."

Zak Elmeghni · Founder

I started my career writing code, solving hard problems, and building systems that improved people's lives. Over 15+ years I noticed something broken — not in the tech, but in the hiring. Companies needed engineers fast, but the process was slow, expensive, and full of bad fits.

The breakthrough was LATAM. The quality was exceptional. The work ethic was strong. The time zones aligned. Yet most companies were missing out because they were stuck with flaky freelancers, bloated dev shops, or didn't know how to navigate nearshore hiring properly.

AI hiring is even worse. The vocabulary moved in 18 months — from "ML engineer" to "LLM engineer" to "agentic engineer." Generalist recruiters can't tell production AI experience from a Coursera certificate. Marketplaces let candidates self-report skills. The result: months of bad interviews, then a hire who's never debugged an agent under live traffic.

We fix that with AI-specific vetting run by AI engineers, a senior-only bench, and a 14-day risk-free trial. We're not a dev shop or a staffing agency. We're your remote hiring partner — and we re-vet our engineers annually against the current frontier of the field.

Get started

Tell us what you're shipping.

One-paragraph brief on your product, stack, role, and timeline. We'll line up matched pre-vetted AI engineers quickly — often within 72 hours.

✓First profiles in 72 hours
✓14-day replacement or refund window
✓Direct line to the founder, no account-management layer
✓No spam, no long sales process

Teams we can help you build

From AI agents to mobile apps, we match you with engineers who have built the kind of systems you are trying to ship.

Need a stack that is not listed?

Tell us what you are building. We will route you to engineers with relevant production experience, not just keyword matches.

Talk to an Engineer →