AI and Machine Learning Recruitment Agency

An AI and ML recruiting agency sources, screens, and delivers interview-ready candidates so you're not spending weeks sifting through applicants on your own.

At Near, the process starts with a discovery call where your dedicated recruiter learns about your team, the specific role, and your technical requirements—whether that's PyTorch versus TensorFlow, experience with LLMs versus classical ML, or MLOps at scale. 

We source from our LatAm network, screen on technical depth and English proficiency, and deliver a shortlist in days.

Then you interview and make the hire. We support you every step of the way to make the process fast and easy on your side.

Near is a full-service nearshore staffing and recruiting partner that helps US companies hire across all industries and functions, including AI engineering, machine learning, data science, and MLOps.

We have recruiters who specialize in placing ML engineers, data scientists, AI engineers, and the full range of roles that AI teams need.

Here's what our services cover:

1. Candidate sourcing and screening: We handle sourcing, English proficiency testing, technical screening, relevant experience verification, initial interviews, and international background checks.
2. Payroll, compliance, and contracts: Through our staffing model, Near can manage contracts, payroll, tax compliance, and benefits administration so you can hire in LatAm without setting up a local entity or navigating local labor law yourself.
3. Equipment and onboarding logistics: If you need it, we make it happen. We've helped clients source laptops to avoid customs delays, secure office space, and coordinate equipment setup.
4. Ongoing support: Once your hire is in place, we don't disappear. Your dedicated recruiter stays available on Slack for questions, check-ins, and anything that comes up.

Both our recruitment-only and full-service staffing options include the same rigorous vetting process and no upfront costs.

Yes, Near can place Generative AI engineers, LLM integration specialists, and prompt engineers, and demand for these roles has grown significantly. 

We source engineers with hands-on experience in LangChain, RAG architectures, OpenAI and Anthropic APIs, vector databases, and fine-tuning workflows.

If you need someone who can build GenAI-powered products rather than just experiment with APIs, that's a profile we can search for and shortlist.

Yes, Latin America has a deep and growing pool of AI and ML professionals with real production experience, strong Python foundations, and familiarity with US engineering standards.

Countries like Argentina, Brazil, Colombia, and Mexico have invested heavily in STEM education over the past decade. 

Major tech companies, including Google, Amazon, and Meta, have established engineering hubs across the region, which means many LatAm engineers have worked alongside or directly for US tech organizations. 

The AI talent pool includes engineers with experience building recommendation systems, NLP pipelines, fraud detection models, and computer vision applications at scale.

What Near adds is the filtering: we find people with genuine depth, not just familiarity, and make sure they're the ones you interview.

Yes. LatAm AI and ML engineers work US business hours because the time zones are naturally aligned.

Engineers in Argentina are never more than 2 hours ahead of New York. Engineers in Colombia align with Eastern and Central time, depending on the time of year.

Your daily standups happen live, your code reviews get turnaround on the same day, and urgent incidents don't wait until morning.

Most clients receive their first qualified shortlist of AI and data professionals within 3 to 5 business days of kickoff, with hires typically closing within 21 days total.

The 21-day timeline begins with the kickoff call and includes shortlisting, interviews, and the offer stage.

For specialized roles like NLP Engineers or MLOps specialists, timelines may extend slightly, but you'll have a realistic picture on the first call.

You can hire an ML engineer through Near from around $3,000 per month, including payroll and compliance support. 

Near's fees depend on which model you choose—recruiting only, or full-service staffing that includes payroll and compliance—and we walk through both clearly on the first call.

There are never upfront costs; you pay only once you make a hire.

Most clients find that even after Near's fees, the total annual cost of a LatAm AI hire is still up to 60% less than the equivalent US-based hire.

Near has a 97% placement success rate, 80% two-year retention across placements, and a 4.9 rating on G2 from 115+ client reviews. Those metrics reflect consistent delivery of quality candidates.

Our recruiters specialize by function, which means they understand what separates a strong data or AI hire from a weak one.

Every candidate goes through structured screening for technical skills, communication ability, and culture alignment before reaching you.

Clients regularly tell us their hardest decision is choosing between candidates, not settling for whoever is available.

If a hire doesn't work out, our 180-day replacement guarantee protects you.

LatAm outperforms traditional offshore hiring in Asia and Eastern Europe because of better communication quality and time zone and cultural alignment.

Time zone gaps kill AI development velocity—a model training issue that needs a quick conversation becomes an overnight delay when your engineer is 10+ hours away in Asia. LatAm eliminates that.

LatAm engineers work your hours, join your standups, and respond in real time.

Communication in AI work is high-stakes: discussing tradeoffs in architecture, explaining why a model is underperforming, pushing back on a bad requirement.

Near only places candidates with fluent, professional English—not "good enough" but genuinely clear and confident.

Finally, cultural alignment matters for strong team building. LatAm engineers have grown up adjacent to US engineering culture: US tech company norms, direct feedback styles, and startup-pace expectations are familiar, not foreign.

That translates to easier onboarding, fewer friction points, and hires who feel like part of the team from week one.

Yes, Near screens specifically for hands-on execution if that’s what you need.

This is a real distinction in data and AI hiring, and it's worth being explicit about on the discovery call. 

Some teams need an architect who can design systems and direct others to build them. Others—especially smaller teams where there's no one else to hand off to—need someone who will actually write the SQL, build the pipeline, configure the automation, and ship the thing.

If “hands on keyboard” is what you're looking for, tell us. 

We screen for production execution history, specific project ownership, and whether a candidate's experience reflects building versus advising, and we only send profiles that match what you've told us you need.

The tension between "senior enough to own the work autonomously" and "not so senior they won't get their hands dirty" comes up often enough that it's worth naming clearly. If that's your concern, it's exactly the kind of requirement we build the search around.

Near only places candidates who are seeking dedicated, full-time, long-term engagements, not contractors splitting their time across multiple clients.

This is a real concern in technical hiring, and we screen for it directly.

We present people looking for a full-time role with one team, not a portfolio of part-time arrangements.

That standard applies to both direct-hire and staffing placements. It is also one of the reasons Near's two-year retention rate sits above 80%—people who are genuinely committed to one role tend to stay.

Demand for AI and machine learning talent has outpaced supply significantly, and the gap is widening.

Data scientists, ML engineers, MLOps engineers, and deep learning specialists are among the most competed-for roles in the US market, with large tech companies, well-funded startups, and enterprises all hiring for the same profiles at the same time.

The result is long time-to-hire, inflated salaries, and a candidate pool where strong engineers have multiple competing offers.

Latin America offers a practical alternative for hiring engineering roles. The region has a deep and growing pool of AI and data engineers—many trained at strong technical universities or with experience at US tech companies—who are available at a fraction of US market rates and work US business hours.

For companies that can't compete with FAANG compensation packages or simply need to move faster than the US market allows, LatAm hiring is how they get the team built.