Why AI Companies Are Building Teams in Latin America

Artificial intelligence companies are growing beyond AI development. As businesses adopt AI solutions, providers need more than engineers building models and products. They also need professionals who can implement technology, support customers, manage projects, troubleshoot integrations, and translate business requirements into practical solutions.

For U.S. AI companies, hiring in Latin America (LATAM) offers an opportunity to build these capabilities with experienced professionals who can collaborate during U.S. business hours, work directly with customers, and integrate into existing teams.

The opportunity goes beyond cost savings. Hiring in LATAM can help AI companies scale customer delivery, expand technical capacity, and support growth efficiently.

1. Access to technical talent beyond AI engineering

When companies think about AI hiring, they often focus on machine learning engineers, AI researchers, and data scientists. But building an AI product is only one part of running a successful AI business.

As AI solutions reach more customers, companies need professionals who can help customers implement the technology, connect it to existing systems, and use it to solve business problems.

These roles include Customer Solutions Engineers, AI Project Managers, Support and Integrations Engineers, Implementation Specialists, and Technical Customer Success Managers.

For example, a Customer Solutions Engineer may translate a customer's business requirements into a technical demonstration. An AI Project Manager may coordinate implementation timelines and technical dependencies. A Support and Integrations Engineer may troubleshoot APIs, configure integrations, and work with engineering teams to resolve complex issues.

These professionals help bridge the gap between AI capabilities and customer outcomes.

For AI companies, the hiring opportunity in LATAM is therefore broader than AI development. It includes the technical and customer-facing talent required to bring AI solutions into real business environments.

2. Real-time collaboration with U.S. teams and customers

AI implementations often require close collaboration between engineering, product, sales, customer success, and customer IT teams.

A customer might need help configuring single sign-on, connecting an API, resolving a platform issue, or adapting an AI solution to a specific workflow. These activities often require real-time communication and quick decisions.

Many LATAM professionals can work during overlapping U.S. business hours, making it easier to collaborate directly with American colleagues and customers.

This overlap can be especially valuable for customer-facing technical roles. Instead of waiting for an issue to move between teams across distant time zones, professionals can join customer calls, investigate problems, and coordinate next steps while the relevant stakeholders are available.

For AI companies delivering complex solutions, that collaboration can help reduce communication delays and improve coordination across the customer lifecycle.

3. Lower hiring costs while accessing a large talent pool

Building a technical or customer-facing team in the United States can require a significant investment, particularly when a company needs several specialized roles at once.

Hiring in LATAM gives AI companies access to a large pool of highly skilled professionals across technical, customer-facing, and operational roles, with the potential to reduce hiring costs by 40%–60% compared with U.S. hiring. Costs vary depending on the country, seniority, technical specialization, and employment arrangement, allowing companies to scale their teams more efficiently without compromising on talent quality.

This can give AI companies more flexibility when expanding customer support, implementation, professional services, and project management capacity.

However, the objective should not be to find the cheapest candidate. AI companies need professionals who can communicate clearly, troubleshoot independently, manage customer expectations, and work effectively with technical teams.

The strongest hiring strategy considers total employment cost alongside experience, skill fit, retention, and the ability to deliver results.

For companies evaluating LATAM hiring, the relevant question is not simply how much a role costs. It is how much capability the company can build within its budget.

4. Scale customer implementation and technical support as demand grows

An AI company may initially rely on a small product and engineering team to handle customer demonstrations, onboarding, integrations, and technical support.

As the customer base grows, this model can become difficult to sustain. Engineers may spend too much time resolving configuration issues, customer-facing teams may lack technical support, and professional services engagements may compete for limited resources.

Hiring dedicated specialists can help distribute these responsibilities.

A Customer Solutions Engineer can support technical demonstrations and advanced use cases. An AI Project Manager can coordinate delivery and manage dependencies. A Support and Integrations Engineer can handle technical investigations, integrations, and reporting.

Building these capabilities allows companies to organize work around the full customer journey rather than expecting engineering to own every technical activity.

For growing AI businesses, this can be an important step toward scaling customer delivery without adding unnecessary pressure to the core product team.

5. Build an embedded team that understands the product

Hiring internationally is not just about finding individual professionals. It is also about integrating them into the company's operating model.

An embedded LATAM team works within the customer's organization, using its tools, following its processes, and collaborating with internal stakeholders. The customer retains responsibility for day-to-day priorities and performance, while a hiring partner can support recruitment and local employment administration.

This approach can be particularly useful for AI companies that need people to develop a deep understanding of their platform, customer requirements, integrations, and internal workflows.

Over time, that familiarity can help professionals handle increasingly complex responsibilities and collaborate more effectively with the rest of the organization.

The goal is to build lasting team capacity - not simply outsource isolated tasks.

Which AI Roles Should Companies Hire in LATAM?

AI companies do not need to limit their LATAM hiring strategy to software development. Several roles can support product adoption, technical delivery, and customer growth.

The right mix depends on the company's product, customer segment, and delivery model. An enterprise AI platform with complex integrations may need more implementation and technical support capacity, while a product focused on business users may require greater investment in onboarding, training, and customer success.

What AI Talent in LATAM Looks Like in Practice

As AI companies grow, scaling successfully requires more than expanding engineering teams. They also need professionals who can manage projects, support customer implementations, solve technical problems, and translate AI capabilities into practical business solutions.

Altrio Consulting has seen this firsthand through its work with an AI training solutions company that needed to scale its team quickly to support growing operational and customer needs. The company partnered with Altrio to hire for three key roles: AI Project Manager, Support & Integrations Engineer, and Customer Solutions Engineer.

Each role addresses a different part of the customer journey, from coordinating project delivery to solving integration challenges and designing technical solutions around customer requirements.

One of the hires is a LATAM-based Support & Integrations Engineer with more than 10 years of experience in technical support, customer service, and technical onboarding, including Level 1 and Level 2 product support for U.S.-based customers.

His responsibilities go beyond traditional technical support. He builds advanced AI simulations and product demonstrations based on customer business goals, manages professional services engagements from scoping through delivery and quality assurance, and investigates complex platform issues to distinguish software bugs from configuration errors.

He also supports single sign-on (SSO), learning management system (LMS), and API integrations across different customer environments. Working closely with Sales and Customer Success, he contributes to technical discussions, implementation planning, and advanced use-case design. In addition, he uses SQL to maintain customer success dashboards and develop reporting for pilots, quarterly business reviews, and strategic analysis.

This combination of technical expertise, customer-facing experience, and delivery ownership illustrates the type of talent AI companies need as they scale. The role connects technical execution with customer outcomes, helping ensure that AI solutions can be implemented, integrated, and supported in real business environments.

For AI companies expanding their teams, this is an important consideration: the people who help customers implement and use AI are just as important to scaling the business as the people who build the technology itself.

Through its embedded hiring model, Altrio helps AI and technology companies access experienced LATAM professionals who can work directly with their teams and contribute to their growth.

How to Build an AI Team in LATAM

Before hiring in LATAM, AI companies should identify the operational bottlenecks they need to solve.

If engineers are spending too much time on customer demonstrations, a Customer Solutions Engineer may be the right addition. If projects are delayed by unclear ownership or competing priorities, an AI Project Manager may provide the coordination needed. If customers are struggling with integrations and technical issues, a Support and Integrations Engineer may be a better fit.

Once the priority roles are clear, companies should define the skills and outcomes expected from each position.

For technical customer-facing roles, this means evaluating communication, troubleshooting, documentation, and the ability to explain complex concepts. For project managers, it means assessing stakeholder management, risk identification, and delivery ownership. For integration specialists, it may include practical experience with APIs, authentication, SQL, and system troubleshooting.

Companies should also establish how new hires will work with existing teams. Clear responsibilities, structured onboarding, escalation paths, and performance expectations help professionals become productive members of the organization.

Finally, choose a hiring model that fits the company's needs. Some businesses want to recruit and employ talent directly. Others prefer an embedded team model in which the professionals work within their organization while a partner supports recruiting, payroll, contracts, and local employment requirements.

The right approach depends on the company's growth stage, internal resources, and plans for building a long-term team.

Build Your AI Team in LATAM with Altrio Consulting

Altrio Consulting helps U.S. technology companies build embedded teams in Latin America, connecting businesses with experienced professionals and supporting recruitment, local employment, payroll, and ongoing HR needs.

Whether you are expanding customer solutions, AI project management, implementation, or technical support, the right team can help your company scale customer delivery while allowing your core engineering organization to focus on product development.

Continue exploring

Looking to build or scale your AI team in Latin America? Explore these guides to learn more about hiring technical talent, building embedded teams, and choosing the right hiring model.

• Unlocking Latin America's Talent Pool — Discover why companies hire in LATAM for technical expertise, time-zone alignment, and cost efficiency.
• Embedded Team Model Review for SaaS Leaders — Understand how an embedded LATAM team can extend your existing organization without becoming a disconnected outsourced function.
• EOR vs. White-Box Outsourcing in LATAM — Compare hiring models and understand which approach fits your company's recruiting and team-building needs.
• LATAM vs. Philippines: The Real Cost of Building Customer-Facing Teams — Compare nearshore and offshore hiring considerations, including cost efficiency, time-zone alignment, and team collaboration.

Ready to explore what a LATAM team could look like for your company?Schedule a 30-minute free conversation with us to discuss your hiring needs, potential roles, and how Altrio can help you find and retain exceptional talent across Latin America.