LATAM: $105K-$145K depending on seniority. US: $259K-$362K+ for same experience. That’s 44-60% savings. The difference is cost of living, not skill, these OpenAI Gym engineers work with the same RL algorithms, training techniques, and environment design principles. Many LATAM OpenAI Gym developers have built production RL systems for US companies.

One senior nearshore OpenAI Gym developer: save $114K-$217K annually. A team of 5: save $771K-$1.08M+ total. Savings come from lower salaries, no US benefits overhead, reduced recruiting fees, and faster hiring. The 97% retention rate prevents constant rehiring costs.

Post requirements (Day 1). Review pre-vetted candidates (Days 2-5). Interview matches (Week 1-2). Hire and onboard (Week 2-3). Total: 2-3 weeks versus 6-12 weeks traditionally. Faster because we maintain a vetted pool of 50K+ developers including nearshore OpenAI Gym developers. No sourcing delays.

Yes. LATAM OpenAI Gym developers work with the same PPO, DQN, SAC algorithms and training frameworks. They’ve trained RL agents, designed custom environments, and debugged training failures. 85%+ are fluent in English. Cost reflects regional economics, a senior OpenAI Gym developer in Buenos Aires costs $105K-$135K versus $250K-$310K in San Francisco.

Yes. 30-90 day trials to evaluate fit with nearshore OpenAI Gym developers. Contract-to-hire starting with specific RL projects. Project-based work with defined scope. Staff augmentation for long-term flexibility. Our 90-day guarantee means if technical fit isn’t right, we replace them at no cost.

US hiring includes 35-45% benefits overhead, 10-15% recruiting fees, onboarding, stock options, and turnover risk (4-6 months salary). Nearshore OpenAI Gym developers through Tecla eliminate most of these with transparent rates and 97% retention. One monthly rate covers everything.

Traditional: 6-12 weeks (sourcing, screening, interviews, negotiation, notice period). Tecla: 2-3 weeks total. You hire 4-10 weeks faster. While competitors spend months sourcing candidates, you’re onboarding a nearshore OpenAI Gym engineer who starts training your RL agents next week.

LATAM: $100K–$140K depending on seniority. US: $252K–$349K+ for equivalent experience. That’s 45–60% savings.

Nearshore Weaviate developers work with the same vectorizer integrations, HNSW tuning approaches, hybrid search configurations, and cluster operations. Many have built production vector search infrastructure for US companies. The cost difference reflects regional cost of living, not technical depth.

One senior hire: save $112K–$209K annually. A team of 5: save $760K–$1.04M+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means cluster knowledge and schema context stays on your team rather than cycling out.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting firms.

Speed comes from maintaining a vetted developer pool of 50K+, which eliminates the sourcing phase that accounts for most of the delay in conventional hiring.

Yes. Latin American Weaviate developers build with the same vectorizer modules, schema design patterns, hybrid search configurations, and cluster management approaches. 85%+ are fluent in English.

A senior Weaviate developer in Buenos Aires costs $100K–$130K annually. The same profile in San Francisco runs $250K–$320K. That gap reflects regional economics, not differences in what they can build or operate.

Yes. 30–90 day trials to evaluate technical fit and team integration before long-term commitment. Contract-to-hire starting with a defined vector search project. Project-based work with scoped deliverables. Staff augmentation for ongoing development.

Our 90-day guarantee means if technical fit isn’t right, we replace them at no additional cost.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, equity expectations, and turnover risk worth 4–6 months of salary.

Hiring nearshore Weaviate developers through Tecla eliminates most of that. One transparent monthly rate, developers manage their regional benefits, and 97% retention keeps your vector infrastructure knowledge intact.

Traditional recruiting: 6–12 weeks from job post to first day. Tecla: 1–2 weeks total. You hire 5–11 weeks faster.

While other teams are still posting jobs and sorting applications, you’re onboarding a nearshore Weaviate developer who starts designing your vector search schema next week.

LATAM: $88K–$120K depending on seniority. US: $226K–$308K+ for equivalent experience. That's 47–61% savings.

Nearshore dbt developers work with the same warehouse platforms, modeling conventions, testing frameworks, and orchestration tools. Many have built production dbt projects for US data teams. The cost difference reflects regional economics, not technical depth.

One senior hire: save $105K–$188K annually. A team of 5: save $690K–$940K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means your dbt model architecture and business logic stays with your team rather than walking out when someone leaves.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, which eliminates the sourcing phase that accounts for most of the delay in conventional analytics engineering hiring.

Yes. Latin American dbt developers work with the same warehouse platforms, dbt Core and Cloud features, modeling patterns, and downstream BI integrations. 85%+ are fluent in English.

A senior dbt developer in Santiago costs $88K–$110K annually. The same profile in San Francisco runs $225K–$280K. That gap reflects cost of living, not a difference in what they can build or maintain.

Yes. 30–90 day trials to evaluate technical fit and how they integrate with your data team. Contract-to-hire starting with a defined modeling or migration project. Project-based work with scoped deliverables for ongoing analytics engineering support.

Our 90-day guarantee means if the technical fit isn't right, we replace them at no additional cost.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary. In analytics engineering, that replacement cost is compounded by the ramp-up time required to understand an existing dbt project's structure and business logic.

Hiring through Tecla eliminates most of that. One transparent monthly rate, developers manage their regional benefits, and 97% retention keeps your transformation layer knowledge intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still sorting through applications, you're onboarding a nearshore dbt developer who starts cleaning up your transformation layer next week.

LATAM: $92K–$128K depending on seniority. US: $239K–$329K+ for equivalent experience. That’s 46–61% savings.

Nearshore legal AI experts work with the same NLP frameworks, legal document types, and extraction approaches. Many have delivered AI systems for US law firms and legal tech companies. The cost difference reflects regional economics, not technical or domain depth.

One senior hire: save $111K–$201K annually. A team of 5: save $740K–$1M+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means legal domain knowledge and model context stays with your team rather than leaving with the expert.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, which eliminates the sourcing phase that accounts for most of the delay in conventional legal tech hiring.

Yes. Latin American legal AI experts work with the same NLP tools, legal document structures, extraction frameworks, and RAG architectures. 85%+ are fluent in English.

A senior legal AI expert in Bogotá costs $92K–$115K annually. The same profile in New York or San Francisco runs $240K–$300K. That gap reflects cost of living, not a difference in what they can build or analyze.

Yes. 30–90 day trials to evaluate technical fit and integration with your legal or engineering team. Contract-to-hire starting with a defined document analysis project. Project-based work with scoped deliverables. Staff augmentation for ongoing development.

Our 90-day guarantee means if the fit isn’t right, we replace them at no additional cost.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary. In legal AI, that replacement cycle is costly because the candidate pool with both NLP skills and legal domain experience is genuinely small.

Hiring through Tecla eliminates most of that. One transparent monthly rate, developers manage their regional benefits, and 97% retention keeps your legal AI expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still posting jobs, you’re onboarding a nearshore legal AI expert who starts working with your document library next week.

LATAM: $88K–$120K depending on seniority. US: $226K–$308K+ for equivalent experience. That’s 47–61% savings.

Nearshore real estate AI experts work with the same property data sources, valuation methodologies, geospatial tools, and analytical frameworks. Many have delivered models for US real estate companies and PropTech platforms. The cost difference reflects regional economics, not domain depth.

One senior hire: save $105K–$188K annually. A team of 5: save $690K–$940K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means market knowledge and model context stays with your team rather than leaving with the analyst.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, which eliminates the sourcing delay that dominates most hiring timelines.

Yes. Latin American real estate AI experts work with the same property data systems, valuation approaches, GIS tools, and analytical platforms. 85%+ are fluent in English.

A senior real estate AI expert in Buenos Aires costs $88K–$110K annually. The same profile in New York runs $225K–$280K. That gap reflects cost of living, not a difference in what they can analyze or build.

Yes. 30–90 day trials to evaluate technical fit and integration with your investment or analytics team. Contract-to-hire starting with a defined analytical project. Project-based work with scoped deliverables. Staff augmentation for ongoing support.

Our 90-day guarantee means if the fit isn’t right, we replace them at no additional cost.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary. In a specialized domain like real estate AI, replacement takes longer because the candidate pool combining technical and domain skills is smaller.

Hiring through Tecla eliminates most of that. One transparent monthly rate, developers manage their regional benefits, and 97% retention keeps your property analytics expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still writing job descriptions, you’re onboarding a nearshore real estate AI expert who starts working with your data next week.

LATAM: $90K–$126K depending on seniority. US: $232K–$322K+ for equivalent experience. That’s 45–61% savings.

Nearshore healthcare AI experts work with the same EHR data systems, clinical NLP techniques, and HIPAA-compliant cloud environments. Many have delivered AI projects for US health systems and health tech companies. The cost difference reflects regional economics, not domain depth.

One senior hire: save $105K–$196K annually. A team of 5: save $710K–$980K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means healthcare domain knowledge stays on your team instead of leaving with the analyst.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted developer pool of 50K+, which eliminates the sourcing phase that accounts for most of the delay in conventional hiring.

Yes. Latin American healthcare AI experts work with the same EHR platforms, medical coding standards, NLP frameworks, and HIPAA-compliant cloud infrastructure. 85%+ are fluent in English.

A senior healthcare AI expert in Buenos Aires costs $90K–$112K annually. The same profile in Boston runs $230K–$290K. That gap reflects cost of living, not a difference in what they can analyze or build.

Yes. 30–90 day trials to evaluate technical fit and how they integrate with your clinical and data teams. Contract-to-hire starting with a defined analysis project. Project-based work with scoped deliverables. Staff augmentation for ongoing analytical support.

Our 90-day guarantee means if the fit isn’t right, we replace them at no additional cost.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary. In a specialized field like healthcare AI, replacement costs are higher because the candidate pool is smaller.

Hiring through Tecla eliminates most of that. One transparent monthly rate, developers manage their regional benefits, and 97% retention keeps your healthcare domain expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still sourcing candidates, you’re onboarding a nearshore healthcare AI expert who starts working with your clinical data next week.

LATAM: $92K–$128K depending on seniority. US: $239K–$329K+ for equivalent experience. That’s 46–61% savings.

Nearshore finance AI experts work with the same modeling frameworks, regulatory environments, and financial data systems. The cost difference reflects regional economics, not technical depth.

One senior hire: save $111K–$201K. A team of 5: save $740K–$1M+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means model context and institutional knowledge stays with your team.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, eliminating the sourcing phase that dominates most hiring timelines.

Yes. Latin American finance AI experts work with the same risk modeling frameworks, fraud detection architectures, forecasting methodologies, and compliance requirements. 85%+ are fluent in English.

A senior finance AI expert in Buenos Aires costs $92K–$115K. The same profile in New York runs $238K–$300K. That gap reflects cost of living, not capability.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary.

Hiring through Tecla eliminates most of that. One transparent monthly rate, experts manage their regional benefits, and 97% retention keeps your financial modeling expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still sourcing candidates, you’re onboarding a nearshore finance AI expert who starts working with your financial data next week.

LATAM: $88K–$122K depending on seniority. US: $226K–$315K+ for equivalent experience. That's 47–61% savings.

Nearshore manufacturing AI experts work with the same industrial data systems, predictive modeling frameworks, and operational analytics approaches. The cost difference reflects regional economics, not technical depth.

One senior hire: save $105K–$193K. A team of 5: save $690K–$970K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means your plant data context and model knowledge stays on the team.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, eliminating the sourcing phase that dominates most hiring timelines.

Yes. Latin American manufacturing AI experts work with the same industrial data systems, time-series modeling techniques, computer vision frameworks, and supply chain analytics approaches. 85%+ are fluent in English.

A senior manufacturing AI expert in Buenos Aires costs $88K–$110K. The same profile in Chicago runs $224K–$280K. That gap reflects cost of living, not capability.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary.

Hiring through Tecla eliminates most of that. One transparent monthly rate, experts manage their regional benefits, and 97% retention keeps your industrial analytics expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still sorting through applications, you're onboarding a nearshore manufacturing AI expert who starts working with your plant data next week.

LATAM: $84K–$120K depending on seniority. US: $219K–$308K+ for equivalent experience. That's 45–61% savings.

Nearshore marketing AI experts work with the same ad platforms, attribution frameworks, audience modeling approaches, and analytics tools. The cost difference reflects regional economics, not analytical depth.

One senior hire: save $99K–$188K. A team of 5: save $680K–$940K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means your attribution models and channel context stay with the team.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, which eliminates the sourcing phase that dominates most hiring timelines.

Yes. Latin American marketing AI experts work with the same ad platforms, attribution methodologies, audience modeling techniques, and analytics frameworks. 85%+ are fluent in English.

A senior marketing AI expert in Buenos Aires costs $84K–$105K. The same profile in New York runs $218K–$273K. That gap reflects cost of living, not capability.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary.

Hiring through Tecla eliminates most of that. One transparent monthly rate, experts manage their regional benefits, and 97% retention keeps your marketing analytics expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still writing job descriptions, you're onboarding a nearshore marketing AI expert who starts working with your campaign data next week.

LATAM: $88K–$122K depending on seniority. US: $226K–$315K+ for equivalent experience. That's 47–61% savings.

Nearshore e-commerce AI experts work with the same platforms, customer analytics frameworks, recommendation architectures, and forecasting methodologies. The cost difference reflects regional economics, not analytical depth.

One senior hire: save $105K–$193K. A team of 5: save $690K–$970K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means your customer data context and model knowledge stays on the team.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, which eliminates the sourcing phase that dominates most hiring timelines.

Yes. Latin American e-commerce AI experts work with the same platforms, customer analytics methodologies, recommendation frameworks, and forecasting approaches. 85%+ are fluent in English.

A senior e-commerce AI expert in Buenos Aires costs $88K–$110K. The same profile in New York runs $225K–$280K. That gap reflects cost of living, not capability.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary.

Hiring through Tecla eliminates most of that. One transparent monthly rate, experts manage their regional benefits, and 97% retention keeps your e-commerce analytics expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still sorting through applications, you're onboarding a nearshore e-commerce AI expert who starts working with your customer data next week.

LATAM: $82K–$114K depending on seniority. US: $212K–$295K+ for equivalent experience. That's 46–61% savings.

Nearshore HR AI experts work with the same HRIS platforms, people analytics frameworks, and workforce modeling approaches. The cost difference reflects regional economics, not analytical depth.

One senior hire: save $98K–$181K. A team of 5: save $650K–$910K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means workforce context and model knowledge stays with your team.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, eliminating the sourcing phase that dominates most hiring timelines.

Yes. Latin American HR AI experts work with the same HRIS systems, ATS platforms, people analytics methodologies, and workforce modeling techniques. 85%+ are fluent in English.

A senior HR AI expert in Buenos Aires costs $82K–$102K. The same profile in New York runs $210K–$265K. That gap reflects cost of living, not capability.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary.

Hiring through Tecla eliminates most of that. One transparent monthly rate, experts manage their regional benefits, and 97% retention keeps your people analytics expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still sourcing candidates, you're onboarding a nearshore HR AI expert who starts working with your workforce data next week.

LATAM: $84K–$118K depending on seniority. US: $219K–$302K+ for equivalent experience. That's 46–61% savings.

Nearshore education AI experts work with the same LMS platforms, learner data models, outcome prediction frameworks, and NLP tools. The cost difference reflects regional economics, not analytical depth.

One senior hire: save $101K–$184K. A team of 5: save $680K–$920K+ per year.

Savings come from lower regional compensation, no US benefits overhead, eliminated recruiting fees, and faster time-to-hire. The 97% retention rate means learner data context stays with your team rather than resetting when someone leaves.

Post requirements (Day 1). Review pre-vetted candidates (Days 2–5). Interview matches (Week 1–2). Hire and onboard (Week 2–3). Total: 2–3 weeks versus 6–12 weeks with traditional recruiting.

Speed comes from a vetted pool of 50K+ developers and data professionals, which eliminates the sourcing phase that dominates most hiring timelines.

Yes. Latin American education AI experts work with the same LMS platforms, data standards, outcome modeling approaches, and NLP frameworks. 85%+ are fluent in English.

A senior education AI expert in Buenos Aires costs $84K–$105K. The same profile in New York runs $220K–$275K. That gap reflects cost of living, not capability.

US hiring carries 35–45% benefits overhead, 10–15% recruiting fees, onboarding costs, and turnover risk worth 4–6 months of salary.

Hiring through Tecla eliminates most of that. One transparent monthly rate, developers manage their regional benefits, and 97% retention keeps your learner analytics expertise intact.

Traditional recruiting: 6–12 weeks from job post to start date. Tecla: 2–3 weeks total. You hire 4–10 weeks faster.

While other teams are still writing job descriptions, you're onboarding a nearshore education AI expert who starts working with your learner data next week.

Staff augmentation means the person joins your team on an ongoing hourly engagement. Tecla remains their employer of record and handles all compliance, benefits, and management overhead. Direct hire means we recruit a candidate who becomes your full-time employee. You pay a one-time placement fee and the relationship is entirely between you and the hire from that point forward.

The fee reflects the complexity of the search. Junior-to-mid individual contributor roles with a broader candidate pool land at the lower end. Senior engineers, specialized AI and ML roles, and leadership searches land higher. We confirm the rate when we scope the role together.

Yes, and many clients do. A common pattern is using LatAm staff aug to build out a cost-efficient engineering team while using US direct hire or fractional to fill senior or specialized roles where domestic presence or timezone alignment matters.

There's no multi-year lock-in. Most engagements run on rolling monthly terms. We'll discuss the structure on the intake call and set up something that makes sense for your situation.

If a placement doesn't work out within the guarantee period, Tecla will replace the candidate at no additional cost. The guarantee window varies by engagement model and seniority. We'll confirm the specific terms in your agreement.

We place across engineering, product, design, data and ML, AI-ready talent, DevOps, and business and growth functions, including roles in marketing, operations, and revenue, across all seniority levels from individual contributors through executive leadership.

For staff augmentation and direct hire, most clients see a vetted shortlist within 1 to 2 weeks of the intake call. Fractional engagements are often active faster, typically within 2 weeks. Timelines vary by role complexity and market conditions.

No. Staff augmentation and direct hire are both contingency-based. You pay nothing until a candidate is placed. Fractional engagements are billed hourly once the engagement begins. We don't charge retainers or search deposits for standard engagements.

Sourcing top AI talent is no easy feat. The process involves identifying engineers and specialists with real AI experience, vetting them for technical depth and AI-readiness, and matching them to your team's stack and culture. Partnering with Tecla means you skip the legwork. We've built relationships with vetted AI engineers, ML specialists, and technical AI leaders across all major US tech hubs and 18 countries in Latin America. Ready to find senior AI talent onshore in the US, or hire Latin American developers for your engineering team? Let's talk.

San Francisco and the Bay Area remain the deepest pool for AI engineers and ML specialists, home to the leading labs and the largest concentration of production AI experience. New York follows closely, especially for AI roles in fintech and enterprise. Seattle brings strong ML infrastructure talent, Boston leans research-heavy thanks to its universities, and Austin, Denver, and Miami are growing fast with more competitive compensation. Many senior AI specialists also work fully remote, which widens your pool well beyond any single hub. Tecla sources coast to coast, so if you're not sure which market fits your role and budget, contact us and we'll benchmark it for you.

Every country in Latin America has its strengths. The largest tech hubs today are Brazil, Argentina, Mexico, and Colombia, with strong AI talent depth in cities like Sao Paulo, Buenos Aires, Mexico City, Bogota, and Medellin. There are also thousands of senior software developers and AI specialists in Costa Rica, Peru, Chile, and Uruguay. Whether you're looking to hire software developers in Latin America or build a cross-border team, the right country depends on the role, budget, time zone overlap, and culture fit. Not sure where to start? Contact us and we'll build a hiring strategy for you, free of charge.

Tecla handles the full process. We tap our network across 18 countries to source candidates that match your stack, seniority requirements, and culture. We run technical vetting and interviews, present you with a shortlist, and once you make a hire, we manage all legal, compliance, and payroll on the ground. You get the talent, without the operational complexity. Whether you need to hire a software development team in Latin America or a single senior engineer, we handle it all.

Remote software developers in Latin America are university-educated, technically strong, and increasingly specialized in modern stacks and AI-adjacent tools. Most senior candidates are bilingual, have worked with US or European teams, and bring 5 to 10 years of hands-on experience. When companies hire backend developers in Latin America through Tecla, they're getting talent that's already been evaluated the way your own team would evaluate them, for technical depth, communication, and culture fit.

This varies based on role, seniority, and city. Mid-level AI engineers in major US tech hubs typically earn $180,000 to $260,000 in total compensation, while senior engineers and ML specialists range from $260,000 to $400,000+, and Heads of AI or technical AI leads at top companies can exceed $500,000. Cities like San Francisco and New York run at the higher end of these ranges, while Austin, Denver, and remote roles often come in 15 to 25% lower. Contact us for specific benchmarks across roles, seniorities, and US cities.

This varies based on role, seniority, and AI specialization. Senior bilingual software developers across the region generally earn $60,000 to $90,000 annually, while specialized AI engineers, ML specialists, and technical AI leaders typically command $90,000 to $140,000 depending on experience. Ranges shift based on whether your role offers local payroll benefits and equity. Contact us for specific salary ranges, whether you're looking to hire LATAM developers in Mexico, Brazil, Argentina, or anywhere else in the region.

Tecla is a full-service AI talent partner covering AI recruiting, staff augmentation, fractional and executive search, all through one network. Companies come to us to hire Latin American developers across the full stack, from ML engineers and backend specialists to hire fullstack developers in Latin America and senior AI leaders in the US. We handle vetting, sourcing, interviewing, and assessment, plus all legal, compliance, and payroll for your nearshore hires.

Tecla has LangChain engineers in the US and Latin America.US-based at US market rates. Nearshore at $45K to $115Kper year, with the same production experience, the sametools, and the same frameworks. Same skill, different cost.You choose what fits.

Post requirements on day one. Review pre-vetted candidates in days 2to 5. Interview matches in week one to two. Hire and onboard in weektwo to three. Total: 2 to 3 weeks versus 6 to 12 weeks traditionally. Wemaintain a vetted pool of 50K+ developers, so there are no sourcingdelays and no sifting through unqualified applicants.

Yes. LATAM LangChain developers build with the same LangChain framework, OpenAI, Claude, vector databases, and prompt engineering techniques. They've architected RAG systems, optimized chains for cost, and debugged production issues. 85%+ are fluent in English. Cost reflects regional economics—a senior LangChain developer in Buenos Aires costs $105K–$130K versus $250K–$300K in San Francisco.

Yes. 30–90 day trials to evaluate fit with nearshore LangChain developers. Contract-to-hire starting with specific features. Project-based work with defined scope. Staff augmentation for long-term flexibility. Our 90-day guarantee means if technical fit isn't right, we replace them at no cost.

We manage all contracts, local compliance, and payment logistics so you do not have to navigate cross-border employment law. You engage one vendor with a straightforward services agreement.

Every placement comes with a 90-day replacement guarantee. If the match is not working, we find you another developer at no additional cost.