AI and Machine Learning Developers in Latin America

In several short years, artificial intelligence (AI) moved from the research labs to mainstream usage. With the rapid advancement in technology capabilities and commoditized access to model development, AI adoption is no longer a yes or no decision—it’s more of a how. And this appears to be a major stumbling block.

Many CIOs feel they’re expected to know more about AI than they currently do, according to a Salesforce survey. Three of five CIOs think stakeholder expectations for their AI expertise are unrealistic. At the same time, most businesses are also struggling to start or scale AI adoption due to skills shortages, data complexity, and ethical concerns.

According to the IBM Global AI Adoption Index, the leading barrier to AI adoption inside a company is talent. Since 2015, the global demand for AI skills has increased globally by 9 times, according to a Citi report. With high demand and limited supply, hiring times are prolonged. The average time-to-fill for AI-related roles is 55 days, according to the same report. Although some vacancies remain unfilled for up to 84 days. With prolonged hiring and small teams, project timelines constantly get delayed.

In addition, building an in-house AI team can be a daunting and costly endeavor. A recent McKinsey study reveals that the average cost of hiring a single AI specialist is a staggering $150,000 annually.

To overcome these barriers, many organizations are turning to nearshoring partnerships—an approach that offers greater predictability and confidence with ML and AI deployment. By partnering with a nearshore AI and machine learning development services vendor in Latin America, you gain access to:

Missing AI skills and competencies
Tested technology frameworks
Lean model development processes (MLOps)
Proven methods for model validation and explainability
Model observability and security
Speed and efficiency in project delivery
More predictable path to ROI

In this article, you’ll discover why Latin America is a top choice for hiring AI developers, how to identify your project needs, and where to find the best talent. By the end, you’ll have a clear roadmap for successfully hiring AI developers in Latin America, complete with a better understanding of the benefits this nearshore region has to offer. Keep reading to learn more!

The Strategic Advantage of Hiring AI Developers in Latin America

Early AI development has been primarily concentrated in the West, but as AI increases in ubiquity, the development and adoption of this technology has quickly gained traction in Latin America, fueled by a combination of factors that are propelling the region to the spotlight of the global tech scene.

Firstly, Latin America’s digital economy is growing twice as fast as the global average, according to a recent study by the Inter-American Development Bank, making it a prime destination for cutting-edge tech talent sourcing. With the region’s tech industry booming, you can gain access to a pool of talented professionals skilled in AI development.

Another reason is the region’s strong focus on education, particularly in the fields of science, technology, engineering, and mathematics (STEM). According to the World Economic Forum, Latin American countries like Brazil, Mexico, and Chile are investing heavily in STEM education, producing a steady stream of highly skilled professionals ready to tackle complex AI projects.

Local governments across Latin America are also implementing policies to promote innovation and entrepreneurship in the tech sector. For example, Mexico recently launched its National AI Strategy, aiming to position the country as a leader in AI development in the region. Similarly, Colombia and Argentina have established themselves as a hub for tech startups, attracting talent from across the region.

Furthermore, large tech companies have continued to expand their footprint in the Global South by establishing research labs, development centers, and engineering offices. IBM was one of the first big tech companies to establish an industry research lab in Latin America, launching research labs in São Paulo and Rio de Janeiro 2010. Developers, engineers, scientists and other experts at these labs are dedicated to advancing the fields of artificial intelligence, hybrid cloud, and quantum computing.

Overall, LATAM’s AI development landscape is vibrant and full of potential, offering a wealth of opportunities for businesses looking to tap into the region’s growing tech talent pool. With a strong foundation in STEM education, supportive government policies, and a collaborative tech ecosystem, Latin America is poised to become a major player when it comes to cost-effective AI development.

Benefits of Hiring AI Developers in LATAM

Hiring AI developers from Latin America can offer your business a range of advantages, making it a strategic decision with long-term benefits. This strategic move can give your business the competitive edge needed to thrive in today’s AI-driven landscape. Here are some benefits of hiring AI developers in Latin America!

1. Cost-Effectiveness

Developing AI solutions in-house can be time-consuming, taking months or even years. In contrast, Latin America is known for offering competitive rates for skilled professionals, allowing you to access top-tier AI talent at a fraction of the cost compared to developed countries. According to a report by HackerRank, Latin American developers rank among the top 10 in the world in terms of cost-effectiveness. All in all, hiring AI developers from Latin America can save you up to 30-50% compared to hiring developers in the US or Europe. This cost efficiency doesn’t come at the expense of quality, either, which makes it even more attractive.

2. Quality of Talent

Latin American developers are highly skilled and often possess strong educational backgrounds in STEM fields. Countries like Brazil, Costa Rica, and Argentina are known for their rigorous engineering programs, producing top-notch AI professionals. Then you also have country-specific tech hubs. Cities like São Paulo, Mexico City, and Buenos Aires are becoming tech hubs, attracting global tech giants and fostering a vibrant startup scene. This growth creates a dynamic environment where AI developers continually hone their skills and stay updated with the latest industry trends.

3. Faster Implementation

Speed is paramount in today’s competitive landscape. Forrester Research found that companies leveraging outsourced AI solutions achieved a significant time-to-market advantage, reaching their target markets 20% faster than those relying solely on internal teams. Nearshore vendors have established development processes and readily available expertise, which significantly expedite project completion.

Nearshore AI development teams typically have tried-and-true methodologies and frameworks that streamline the AI development lifecycle. These methodologies encompass everything from initial data collection, to preprocessing to model training, to validation, and deployment. Having these processes pre-established, can help businesses avoid common pitfalls and delays associated with in-house development.

3. Time Zone Compatibility

Latin America’s time zones are generally close to those of North America, making collaboration more efficient. With your team only being a few hours ahead of schedule you can always find time to meet and work together in real time. This proximity can lead to quicker smoother communication and better alignment with your business hours, allowing for faster project turnaround times.

4. Cultural Compatibility

The cultural compatibility between Latin American developers and North American businesses also plays a vital role in the popularity of outsourced AI development within this region. Shared business values and similar work ethics facilitate seamless integration into your existing teams. This cultural fit can lead to better collaboration, higher productivity, and fewer misunderstandings when working with cutting-edge technologies like artificial intelligence.

5. Language Skills

Nearshore AI solutions allow you to work with developers who have minimal language barriers. Even within the countries with the lowest EF EPI Index ranking, most AI developers in Latin America who work on international projects are fully proficient in English, as they have experience collaborating with teams from English-speaking countries. This will ensure better collaboration and make you feel like they’re a properly integrated member of your team.

6. Growing Tech Ecosystem

Latin America’s tech ecosystem is rapidly expanding, with cities like São Paulo, Mexico City, and Buenos Aires becoming key tech hubs. This growth means access to a vibrant community of developers, networking opportunities, and a supportive environment for innovation with AI software products and applications.

Challenges of Nearshore AI/ML Development

Nearshoring software development in general is a complex process, as it requires a strong alignment between the partners, proactive expectation management, and knowledge exchange. With these elements in place, nearshore initiatives have a high chance of success.

When it comes to nearshoring AI/ML development, partners should aim to foster an effective service delivery framework that accounts for the following challenges:

Knowledge Transfer

The main cause of AI project failures is misunderstanding or miscommunication among stakeholders about the problems needed to be solved using AI.

Artificial Intelligence and Machine Learning have a number of proven use cases in agriculture, healthcare, finance, travel industries, among others—and even more yet untapped opportunities. However, the technology’s flexibility is a double-edged sword: Amidst the myriad options, it may be hard to figure out the optimal use case and then communicate it to a nearshore team in LatAm.

To formalize and communicate your vision effectively, think of the specific business or user problems you want to solve first. Think in terms of specific outcomes. Let’s say you want to improve billing with AI. List specific processes and use cases the new system should enable e.g., automated bill assignment, claim error detection, and revenue forecasting. Decide on the success criteria of such features e.g., 99.99% automatic assignment for all standard queries; mean claim error rate of less than 5%, etc. Then work with your partner on determining the feasibility of each use case and defining the optimal AI approach (e.g., supervised machine learning vs deep learning vs generative AI).

Typically, an AI/ML nearshore vendor will guide you through this stage during the discovery—a structured, pre-engagement process, designed to clarify project requirements and develop a roadmap for subsequent machine learning app development.

Data Privacy Concerns

Data is the backbone of AI/ML model development. Yet, it’s also in short supply either due to regulatory limitations or internal concerns over sharing corporate insights with a third-party vendor.

The optimal approach to minimizing regulatory and security risks is to apply appropriate data minimization and anonymization techniques to all model training data shared with other parties. Common privacy-preserving techniques for AI model training include data masking, homomorphic encryption (HE), differential privacy, secure multiparty computation (SMPC), and federated learning.

Another approach is to train AI models on synthetic data—computer-generated mock data that closely mimics real-world data structures. Synthetic data sets eliminate any privacy risks without compromising algorithm efficiency. In fact, scientists have found that synthetic datasets help build better models in industries like healthcare or finance, where original data is in limited supply.

Integration Issues

Potential incompatibility with existing systems is another major concern over externalizing machine learning software development. Indeed, integration problems can emerge when the nearshore vendor is given limited context about your existing technology portfolio.

Again, such issues can be mitigated through software requirements specification at the early stage of the project. By providing your partner with comprehensive details about your IT landscape, you can determine the necessary integration requirements at the model design stage. An experienced partner will also advise you on the necessary adjustments to cloud infrastructure or data management processes to ensure a smoother roll-out and effective model performance in production.

Moreover, many AI solutions can be served as serverless application programming interfaces (APIs) using cloud services providers. This approach abstracts the complexities of infrastructure management. Instead of worrying about resource provisioning, scaling, and maintenance, your teams can focus on further model interaction and fine-tuning.

Identifying your AI Development Needs

Before you start hiring AI developers in Latin America, it’s crucial to identify your specific AI development needs. This step will help you find the right talent and ensure that your AI projects are successful.

1. Determine the Scope of Your AI Project

Start by clearly defining the goals and objectives of your AI project. Identify the specific tasks and functionalities you want your AI system to perform. This will help you narrow down the skill set and expertise required from AI developers.

2. Key Skills to Look for in AI Developers

Depending on your project requirements, you may need AI developers with expertise in machine learning, natural language processing (NLP), computer vision, or other AI subfields. Look for developers who have experience with the tools and technologies relevant to your project.

Common Skills Across Roles for AI developers:

Python
AWS
Java
SQL

Role-Specific Skills:

Data Engineer:
- Spark
- Scala
- Kafka
- ETL
- Airflow
- Snowflake

Machine Learning Engineer:
- NLP
- TensorFlow
- Deep Learning
- Pytorch
- Computer Vision

Business Analyst:
- Tableau
- Looker
- Data Analysis
- R
- Microsoft Excel
- ETL
- Data Warehousing
- Financial Modeling

3. Tools and Technologies Commonly Used in AI Development

Familiarize yourself with the tools and technologies commonly used in AI development, such as TensorFlow, PyTorch, and scikit-learn. Understanding these technologies will help you communicate effectively with potential AI developers and assess their skills accurately.

Hiring AI developers in Latin America can be a strategic move for businesses aiming to leverage cutting-edge technology while optimizing costs. The region is emerging as a hub for skilled AI professionals, offering a unique blend of talent, competitive pricing, and a supportive tech ecosystem.

Using Nearshore Staff Augmentation Agencies to Hire AI Developers

Nearshore staffing agencies can be valuable partners in your search for AI talent in Latin America, making the entire process easier for you. Here’s how they can help:

Access to a Large Pool of Pre-Screened Talent:

Nearshore staff augmentation agencies have access to a wide network of AI developers who have been pre-screened and vetted for their skills and experience. This can save you time and effort in finding qualified candidates for your AI projects.

Industry Knowledge and Expertise:

Recruitment firms specialize in understanding the tech industry and can provide valuable insights into the current market trends and salary expectations for AI developers. They can also advise you on the best hiring strategies for your specific needs.

Faster Hiring Process:

If there’s anyone guaranteed to have the right resources and expertise to expedite the hiring process, it’s staff augmentation companies. They can quickly identify suitable candidates, conduct initial screenings, and coordinate interviews, allowing you to fill AI positions faster.

Assistance with Negotiations:

Recruitment agencies can help facilitate negotiations between you and the AI developers you wish to hire. They can help ensure that both parties are satisfied with the terms of employment, leading to a smoother hiring process.

Ongoing Support:

IT staffing firms provide ongoing support even after the hiring process is complete. They can help with onboarding, training, and performance management, ensuring that your AI developers are set up for success.

How to Ensure Successful Cooperation With a Nearshore AI Development Services Provider

Assuming you’ve already found the right artificial intelligence nearshore vendor, here’s how to structure the collaboration process for maximum efficiency.

1. Focus on Building Trust

Trust is the bedrock of any successful partnership, but it’s not something that comes as a “default”.

Research shows that trust among teams is based on a combination of different surface-level cues (initial impressions and certain biases), emotional reactions (e.g., feelings of disappointment due to unmet expectations), and deeper-level schemas and cues (observations, developed over time). The latter are the most important components as they’re used to assess the three most important dimensions of trustworthiness: ability, benevolence, and integrity.

There are several ways to speed up the process of building trust between internal stakeholders and external IT outsourcing services providers.

Define the key objectives. Establish the main project milestones, deadlines, and success criteria. Having clarity and alignment in vision reduces misunderstanding, which reduces trust. Additionally, it facilitates the development of a shared belief that others are pursuing the same goal with good intentions.

Establish two-way feedback mechanisms to collect feedback from internal stakeholders and your vendor. When problems arise, tackle them head-on. Open discussions about challenges demonstrate a willingness to work together to find solutions, cultivating trust.

Focus on the outcomes, not approaches. Some companies fall into the trap of focusing too much on how the service should be delivered, rather than the outcomes. However, by being too prescriptive with your recommendations, you risk missing out on the extra value your partner can bring (e.g., a more agile process, new insights for technology roadmap planning, etc.). Consider adapting to the provider’s model and best practices to capture more value from the partnership.

Remember: You already have many agreed-upon processes and milestones formalized in legal documents like master service agreement (MSA) and scope of work (SoW). Hence, your goal is to give them a “headspace” to show their best abilities and effectively deliver on the said work without much friction.

2. Foster a Collaborative Partnership

Successful IT nearshoring partnerships are no longer transactional (i.e., driven solely by cost reduction), but rather outcome-oriented.

Such collaborative models provide access to the missing competencies and the ability to unlock new revenue streams through co-innovation. Yet, the collaborative nature of IT partnership also calls for a greater integration in the value chain. If your partner doesn’t understand the “bigger picture” – your business direction and key priorities – they won’t be able to align well with your strategy.

Sharing regular updates on your goals and being candid about more complex strategic decisions will help your partner better understand their role in the outcome and adjust their performance accordingly.

3. Establish Effective Knowledge Sharing Processes

A certain communication gap is inevitable in IT outsourcing. Your vendor will lack the institutional knowledge of in-house teams and will need time to understand your IT landscape, existing processes, and communication preferences. Your goal, however, is to proactively bridge the chasm in communication rather than allow it to widen further due to limited knowledge sharing.

To ensure effective knowledge transfer processes:

Create shared repositories. When you outsource machine learning and AI model development, effective access to data is key. Provide access to pre-approved datasets, set up shared code repositories and model registers to keep track of ongoing experiments and ensure proper version control.

Maintain clear documentation. Detailed records are essential for effective experiment recreation and reverse-engineering issues with model performance. Encourage everyone on the team to keep up-to-date notebooks and create shared technical documentation.

Schedule regular collaborative sessions like sprint reviews and project retrospectives, where everyone can candidly discuss successes, collaborate on solutions to recent mishaps, and get in sync on the next steps.

Beyond that, incentivize knowledge sharing through less formal initiatives—Slack discussion channels, group workshops, and shared corporate Wiki pages. Doing so helps enhance alignment, collaboration, and trust between distributed teams and drive more innovative thinking.

4. Select the Right Metrics to Measure Vendor Performance

As mentioned already, the ROI of AI investments can be elusive, unless backed by the right measures. Our recommendation is to track both technical model metrics and business KPIs.

Technical AI/ML model metrics like ‘accuracy’, ‘recall’, or ‘error rate’ can be directly influenced by your AI development partner, thus creating good measures for mutual accountability. On the other hand, business metrics like ‘adoption rates’, ‘productivity gains’, or ‘percentage of workflow automation’ are in the shared zone of responsibility. Your partner can influence them, but also require certain efforts on your side (e.g., staff upskilling to accelerate adoption).

Still, tracking and sharing these metrics with your partner is important as it provides them with extra valuable context for advisory. For example, if the adoption rates stall at some point, the vendor can leverage user feedback to create new user stories and prioritize improvements in the product’s UI or functionality.

Ready To Hire AI Developers in Latin America?

For many businesses, AI development remains uncharted territory due to limited experience with effective data management, algorithm selection, and validation, plus model explainability. As the region’s tech ecosystem continues to thrive, the demand for these developers will continue to increase. So it’s best to hire them as early as possible! Now, if you’re looking to hire AI developers in Latin America, consider partnering with us at Next Idea Tech. With our extensive network of skilled AI professionals and strong expertise in the Latin American tech market and years of nearshoring experience, we can help you find the right talent for your AI projects.

By partnering with Next Idea Tech, you gain access to:

Missing AI skills and competencies
Tested technology frameworks
Lean model development processes (MLOps)
Proven methods for model validation and explainability
Model observability and security
Speed and efficiency in project delivery
More predictable path to ROI

By seamlessly integrating into your team, our team of skilled AI and machine learning developers will empower your business to remain competitive in the era of rapid technological advancements without the overhead of building massive internal engineering departments.

Skills

Posted on

December 13, 2024