7 AI Agents Developer Jobs Sources for LATAM

A strong search for AI agents developer jobs combines a LATAM-focused marketplace such as LATOjobs with technical communities, specialized AI boards, company career pages, startup channels, and targeted recruiter outreach. The market already includes 2,359 open agentic AI roles across 3,520 tracked AI roles at 197 hiring companies, with agentic positions representing 67% of that tracked sample.

Where AI Agents Roles Actually Surface

AI agents developer jobs rarely use one standard title. Search for AI agent engineer, agentic AI developer, LLM engineer, autonomous systems developer, applied AI engineer, and software engineering roles that mention tool use, RAG, orchestration, or production deployment.

The demand is moving beyond chatbot prototypes. Reuters reported that demand for forward-deployed engineers and similar roles grew 42-fold globally from 2023 to 2025, although only about 9,000 roles were created worldwide, according to the Agentic AI Jobs Index. These roles sit between engineering, deployment, customer implementation, and production operations.

Candidates in São Paulo, Mexico City, Buenos Aires, Bogotá, Santiago, and Lima should judge every source against the same questions:

• Does the role have a real technical signal?
• Can someone in your country apply?
• Is the work remote, hybrid, or tied to a hiring location?
• Does the employer disclose compensation?
• Is the company operating production agentic systems, or only experimenting?

Start with software engineering jobs on LATOjobs, then add the channels below. Each source provides a different signal, from open-source proof to frontier-lab specialization, startup access, recruiter demand, and LATAM-focused matching. For broader search tactics, see this guide on how to find AI remote jobs.

1. Hugging Face Jobs and Community

Hugging Face is useful because it combines job discovery with visible technical work. Its model cards, datasets, repositories, discussions, and community activity give candidates a way to demonstrate ability before a recruiter opens a résumé.

For an AI agent developer in Brazil or Argentina, that matters. A public project that connects an LLM to tools, retrieves information from a controlled knowledge base, records traces, and handles failed actions can communicate more than a list of framework names. The project doesn't need to be large. It needs a clear README, setup instructions, evaluation examples, limitations, and a short explanation of the security decisions.

The community also helps you identify organizations working close to the technical frontier. Follow company organizations, monitor announcements, and participate in discussions where developers explain implementation choices. Recruiters often look for evidence that a candidate can reason about systems, not just repeat model terminology.

Turn community activity into evidence

Use Hugging Face deliberately:

• Publish a focused project: Show tool calling, RAG, memory boundaries, or multi-agent coordination instead of a generic chat interface.
• Explain the evaluation loop: Document test cases, failure modes, human review, and how you decide whether an answer or action is acceptable.
• Follow relevant organizations: Track hiring announcements from teams building models, infrastructure, and agent applications.
• Name tools accurately: Mention LangChain, AutoGen, LlamaIndex, or another framework only when you can explain what you built with it.
• Check frequently: Community roles can be specialized and may close before they reach broad distribution.

Salary information varies sharply by location, contract structure, seniority, and employer. Treat any range shown in a visual or job post as a prompt to verify the employment model, payment currency, benefits, and tax responsibilities before applying.

2. Anthropic OpenAI and AI Research Lab Career Pages

Frontier-lab career pages provide a high-specialization signal. Anthropic, OpenAI, Google DeepMind, Meta AI, and similar organizations publish roles involving agent architectures, model behavior, evaluation, infrastructure, and applied deployment.

These employers usually expect more than prompt writing. Their technical interviews and screening materials can test distributed systems, Python or another production language, data pipelines, model evaluation, reliability, and the ability to work with ambiguous product requirements. A candidate from Mexico City or Santiago should read the team description carefully because the same “AI engineer” label can describe research engineering, platform work, safety, or customer-facing implementation.

Direct applications can also expose requirements that disappear in aggregated listings. Check whether the role accepts candidates in your country, requires relocation, supports international employment, or offers contractor arrangements. Visa sponsorship and remote policies vary by team and can change.

Prepare for specialist screening

Before applying, study the organization's public research, product documentation, and engineering writing. Build a portfolio project that reflects the team's work without copying proprietary systems. For example, demonstrate bounded tool execution, retrieval quality checks, prompt-injection defenses, structured outputs, and recovery after a failed tool call.

A senior enterprise posting from Johnson & Johnson illustrates how specific the bar can become. Its Senior AI Engineer role asks for agentic AI development and frameworks including LangChain, LangGraph, and LlamaIndex, with an anticipated base pay range of $109,000 to $174,800 USD in the United States, as shown in the company's job posting.

That range isn't a LATAM benchmark. Use it only as context for a U.S. senior role, then compare the actual offer structure available to you. A contractor in Bogotá, for example, may receive a different gross amount and carry different tax, benefit, and equipment responsibilities than a U.S. employee.

3. Wellfound and Startup Job Boards

Startup channels reveal product urgency. An early-stage company hiring an AI agent developer may need one engineer to connect models, design tool interfaces, build a retrieval layer, ship APIs, monitor usage, and speak with customers. That breadth can accelerate your learning, but it can also create unclear scope and uneven support.

Wellfound and similar startup boards are useful for finding companies building customer-support agents, research assistants, workflow automation, and multi-agent orchestration products. Search by company stage, industry, remote policy, and location. Look for teams that can explain the customer problem, the current product, and why an agent is appropriate.

A startup based in São Paulo may hire locally, while a Mexico City or Buenos Aires company may build a distributed team. U.S. startups may recruit nearshore engineers for overlapping working hours. Don't assume “remote” means “available everywhere.” Confirm the countries accepted, expected schedule, payroll model, and communication language.

Evaluate the opportunity, not just the title

Ask direct questions during the first conversation:

• Product maturity: Is the agent serving real users, or is it still a demonstration?
• Technical ownership: Will you own evaluation, observability, and deployment, or only prompt workflows?
• Runway and priorities: Can the founders explain the hiring reason and near-term product goals?
• Equity terms: What is the vesting schedule, what type of equity is offered, and what information can the company provide?
• Working arrangement: Is the role employment, contracting, or an arrangement through a local entity?

Startup compensation often combines cash with equity. Don't value equity as guaranteed income. Compare the cash component with your local cost structure and with the risk of an early-stage employer. A role with a lower nominal salary can be worthwhile if it gives you meaningful production ownership, but only if the responsibilities and support are clear.

Network with founders and accelerator communities in São Paulo, Mexico City, Buenos Aires, Bogotá, and other regional hubs. A thoughtful technical conversation can uncover roles before a formal posting appears.

4. LinkedIn and Targeted Recruiter Outreach

LinkedIn provides a demand signal from the people actively sourcing talent. Search for AI agents engineer, agentic AI developer, LLM engineer, autonomous systems developer, RAG engineer, and AI platform engineer. Then filter by company, location, remote eligibility, and seniority.

Your profile must make the recruiter's decision easy. Put the target role in the headline, describe the systems you've built, and link to one strong repository or deployed demonstration. “Worked with AI” is weak. “Built a tool-using support agent with retrieval, structured actions, trace logging, and human approval for risky operations” gives the reader something concrete to assess.

Set your location accurately and state your timezone. A bilingual candidate in Bogotá or Lima should specify English and Spanish proficiency, preferred working hours, and whether they can work with North American or European teams.

Make outreach specific

Don't send a generic message to every recruiter. Mention the team's product, the role's technical requirements, and your closest evidence. Ask whether the position is open to candidates in your country, whether it is employment or contract work, and how the team evaluates production agent experience.

A recruiter may contact you for a role that sounds senior but pays at an early-career level. U.S. listings collected by ZipRecruiter report average annual pay of $47,930 for AI agent developer roles, with most workers earning between $34,000 and $51,000 and top earners reaching $60,000, according to its AI agent developer salary page. Treat this as U.S. market context, not a guaranteed offer for candidates in Brazil, Colombia, or Peru.

Use this guide to optimizing your LinkedIn profile to improve the headline, project evidence, and recruiter-facing summary. Your profile should support the application, not replace a technical portfolio.

5. Specialized AI and ML Job Boards

Specialized boards reduce generalist noise. Kaggle Jobs, MLOps.community, ai-jobs.net, and similar channels attract employers looking for machine learning, data, infrastructure, evaluation, and applied AI talent.

The strongest roles often use language such as production ML, agent deployment, tool use, RAG, evaluation, observability, and cloud infrastructure. That language signals a systems role rather than a prompt-only position. Candidates in Chile, Argentina, and Brazil should search adjacent titles because companies may classify agent work under ML platform, applied AI, or MLOps.

A Kaggle profile can help when it shows disciplined experimentation, clear notebooks, and reproducible results. MLOps.community is more relevant when your portfolio covers deployment, monitoring, data quality, and operational ownership. An AI-specific board may surface research engineering and applied roles that don't appear in regional searches.

Match the board to your proof

Use a different application asset for each signal:

• Research signal: Explain an experiment, baseline, evaluation method, and limitation.
• MLOps signal: Show deployment, logging, versioning, rollback, and cost controls.
• Agent signal: Demonstrate tool schemas, permissions, state management, and recovery.
• Product signal: Connect the agent's behavior to a measurable user workflow without inventing performance claims.

Core hiring signals identified in a 2026 AI engineering skills roundup include evaluation discipline, observability, RAG, multi-agent orchestration, cost optimization, tool-calling design, memory architecture, and prompt-injection defense, as described by AY Automate's AI engineering skills roundup. Build projects around those capabilities instead of collecting framework badges.

For a broader remote ML search, use this guide to find remote machine learning jobs. You can also skip the crowd by targeting smaller technical communities and applying soon after a role appears.

6. GitHub Dev.to and Developer Community Boards

Developer communities show how you work before an interview. GitHub, Dev.to, Indie Hackers, and open-source project communities attract technical founders and engineers who care about implementation quality.

GitHub is especially valuable for AI agents developer jobs because recruiters can inspect the evidence directly. Pin two or three projects. Each repository should include architecture notes, local setup, environment requirements, sample inputs and outputs, test commands, known limitations, and a section explaining where human approval is required.

Don't publish three nearly identical chatbots. Build a portfolio with distinct engineering signals:

• A tool-using agent: Include strict schemas, permissions, input validation, and error handling.
• A retrieval system: Show chunking decisions, source attribution, retrieval tests, and behavior when evidence is missing.
• An operational project: Add traces, evaluation cases, cost awareness, and a rollback or recovery path.

Write for technical readers

A Dev.to post can explain why you selected LangChain, CrewAI, or another framework, but the framework isn't the achievement. Explain the trade-off. Discuss what failed, how you tested it, and what you'd change for a production environment.

Open-source contributions can create stronger connections than passive profile activity. Fix documentation, improve examples, reproduce an issue, or submit a focused pull request. Follow maintainers and founders who build agent infrastructure, then start conversations around the technical problem rather than immediately asking for a job.

A short technical walkthrough can also support a recruiter conversation. Show the repository, architecture diagram, evaluation approach, and a five-minute demonstration. This format helps a hiring manager distinguish a developer who understands agent systems from someone who has only followed a tutorial.

Use this video as an additional reference for presenting agent development work:

7. LATOjobs and Regional LATAM Tech Hiring Platforms

LATOjobs gives candidates a regional starting point. It connects professionals in Brazil, Mexico, Argentina, Colombia, Chile, Peru, and other markets with local, remote, hybrid, and international opportunities. For AI agents developer jobs, that location context matters because the same technical role can have different employment models, salary structures, and timezone expectations.

The platform includes software engineering, data science, AI, and related categories. It also lists roles that directly match this topic, including an AI Engineer Agentic SDLC opening in Brazil. Candidates should search by both title and category because employers may describe agent work as AI engineering, applied machine learning, software engineering, or automation.

Regional matching also makes employer details easier to evaluate. A candidate in Rio de Janeiro may prefer a Brazilian employment arrangement, while someone in Guadalajara or Córdoba may target an international contractor role. Check the company profile, hiring location, remote conditions, required language, and salary disclosure before investing time in an application.

Build a profile for nearshore hiring

Your LATOjobs profile should state:

• Technical scope: Python, APIs, RAG, tool calling, orchestration, evaluation, cloud, and observability.
• Production evidence: What you deployed, monitored, secured, tested, or supported.
• Communication fit: English and Spanish proficiency, plus Portuguese where relevant.
• Location and schedule: City, country, timezone, and overlap with the employer's team.
• Compensation expectations: Desired currency and whether you're comparing employment or contract offers.

Use remote jobs on LATOjobs to understand the platform's search and career resources. Apply quickly to roles that match your skills, but don't skip verification. Confirm whether the employer hires directly, uses a local partner, or expects independent contracting.

AI Agents Developer Jobs: 7-Platform Comparison

Platform / ItemImplementation Complexity 🔄Resource Requirements ⚡Expected Outcomes 📊⭐Ideal Use Cases 💡Key Advantages ⭐Hugging Face Jobs & CommunityMedium 🔄, active technical contributions help visibilityModerate ⚡, time to build profile, contribute models/reposHigh relevance 📊; targeted AI/agents roles; salary context $120k–$200k ⭐Network with AI peers; discover open‑source roles; recruiter visibilityTargeted AI audience; strong technical signal; access to models/docs ⭐Anthropic, OpenAI & Frontier LabsHigh 🔄, rigorous interviews and selectionHigh ⚡, research background, publications, deep portfolioVery high impact 📊; frontier research roles; $180k–$350k+ with equity ⭐⭐Senior research/production roles; cutting‑edge agent developmentTop compensation, proprietary tech, prestige, strong networking ⭐⭐AngelList (Wellfound) & Startup BoardsLow–Medium 🔄, varied but faster processesModerate ⚡, startup fit, equity understandingGood upside 📊; faster hires; $80k–$160k + equity ⭐Early‑stage startups; LATAM‑friendly remote roles; equity-minded candidatesEquity potential, remote‑first culture, faster hiring cycles ⭐LinkedIn & Recruiter OutreachLow 🔄, profile setup easy but high outreach noiseModerate ⚡, time to optimize profile and engage recruitersHigh volume 📊; broad salary range $100k–$250k; variable quality ⭐Broad job discovery; passive sourcing; targeted recruiter engagementLargest professional network; high recruiter activity; LATAM reach ⭐Specialized AI/ML Job Boards (Kaggle, MLOps, ai-jobs.net)Medium 🔄, requires community engagement and domain signalsModerate ⚡, maintain Kaggle/GitHub, technical portfolioHigh match quality 📊; roles $110k–$200k; technical hires prefer these boards ⭐ML/agents‑specific roles; infrastructure and production ML hiresLow noise, technical depth, higher candidate-role fit ⭐GitHub Jobs, Dev.to & Developer CommunitiesMedium 🔄, public contributions and content boost visibilityModerate ⚡, maintain GitHub projects and technical writingHigh signal to engineers/founders 📊; salaries $90k–$180k; startup emphasis ⭐Open‑source hiring; founder‑led startups; technical contributor rolesValues public code; direct founder access; faster technical vetting ⭐LATOjobs & Regional LATAM PlatformsLow 🔄, regionally focused; profile creation requiredLow–Moderate ⚡, highlight bilingual/timezone and regional fitStrong for LATAM candidates 📊; $100k–$200k for remote roles; regional advantages ⭐LATAM developers seeking nearshore/remote opportunitiesLATAM‑specific, salary transparency, nearshore hiring focus ⭐

Turn Seven Sources Into a Focused Search

Use the seven channels as a system, not as seven separate tabs. Start with LATOjobs filters for country, city, category, and remote work. Search São Paulo, Rio de Janeiro, Mexico City, Guadalajara, Buenos Aires, Córdoba, Bogotá, Santiago, and Lima when the role allows location-based hiring. Save relevant searches for AI, data science, software engineering, and agentic work.

Use Hugging Face, specialized AI boards, and developer communities to strengthen your technical signal. These channels help you find teams that care about open-source work, evaluation, deployment, and infrastructure. Your portfolio should contain two or three documented agent projects, not a long list of unfinished demos.

Monitor frontier-lab and startup career pages separately. Frontier labs may require relocation, sponsorship, or highly specialized experience. Startups may offer broader ownership but less predictable scope and compensation. Read the role description for the actual work, then compare it with your strongest evidence.

LinkedIn outreach should name the team, product, role, and timezone fit. Ask whether the employer accepts candidates from your country and whether the role is employment or contracting. Don't assume that a remote label means global eligibility.

Make every application verifiable

Use tools with integrity. If you list LangGraph, LlamaIndex, AutoGen, or LangChain, be ready to explain the architecture and your personal contribution. Employers increasingly need engineers who can operate agents safely, including secure tool execution, monitoring, evaluation, rollback, and recovery.

Compensation requires the same discipline. Compare USD salary context with contract structure, location, taxes, benefits, paid time off, equipment, and currency risk. The Stanford AI Index coverage summarized by Second Talent reports that agentic AI skills rose from 0.06% of U.S. job postings in 2024 to 0.23% in 2025, representing roughly 90,000 postings, as described in its labor-market analysis. The growth is real, but it doesn't make every role accessible to entry-level candidates.

The market is selective. A June 2026 hiring dataset recorded 2,072 open agentic AI roles across 221 companies, representing 64% of tracked AI hiring, while only 8% were remote, according to the June 2026 agentic hiring report. For candidates in Lima, Medellín, or Córdoba, remote eligibility must be verified before treating a listing as a realistic target.

Follow a weekly routine:

• Monday: Check LATOjobs, specialized boards, and saved searches.
• Tuesday: Improve one portfolio README, test, or evaluation case.
• Wednesday: Review frontier-lab and startup career pages.
• Thursday: Send a small number of specific recruiter or employee messages.
• Friday: Apply to the best matches and record location, contract type, salary, and next steps.

Read LATOjobs career and salary insights to keep your search aligned with regional hiring conditions. Candidates who combine technical proof with careful location and compensation checks will make better decisions than those who chase every listing containing “agent.”

LATOjobs brings together LATAM opportunities across software engineering, AI, data, and related fields, with location filters and salary information when employers disclose it. Visit LatoJobs to search for roles that match your country, city, technical profile, and remote preferences.