Hire LLM Developers
Connect with elite nearshore LLM developers from Latin America in 5 days, at a fraction of US costs. Build your AI engineering team while saving up to 60%, without compromising on quality or timezone compatibility.
LLM Developers Ready to Join Your Team
Why Hire LLM Developers Through Tecla?
5-Day Average Placement
We match you with qualified LLM developers in 5 days on average, not the 42+ days typical with traditional recruiting firms.
Top 3% Acceptance Rate
Only 3 out of every 100 applicants make it through our vetting process. You get developers who've already proven themselves building production LLM applications.
Save 60% on Salaries
Hire senior LLM engineers at 40-60% less than US rates without sacrificing quality or experience level.
97% Retention After Year One
Our placements stick. Nearly all clients keep their developers beyond the first year, proving the quality of our matches.
Zero Timezone Hassle
Work with developers in timezones within 0-3 hours of US hours. No more waiting overnight for responses or debugging API issues solo.
What Our Clients Say
What Our LLM Engineers Build For You
Hire LLM Developers in 4 Simple Steps
Tell Us What You Need
Review Pre-Vetted Candidates
Interview Your Top Choices
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What is an LLM Developer?
An LLM developer builds applications powered by large language models like GPT-4, Claude, or Llama. Think of them as software engineers who specialize in making AI models useful in real products,not research scientists training models from scratch.
The difference from general AI engineers? LLM developers know the practical side of working with foundation models. They understand prompt engineering, RAG architectures, API cost optimization, and how to handle model limitations in production.
These folks sit at the intersection of backend engineering, ML engineering, and product development. They're not just calling APIs,they're building systems that route requests intelligently, cache responses, handle failures gracefully, and keep costs reasonable.
Companies hire LLM developers when they're adding AI features to existing products, building AI-native applications, or scaling prototype demos into production systems. The role exploded when foundation models became good enough to power real products instead of research demos.
When you hire LLM developers, you get AI features that actually work in production. Most companies see faster development cycles, lower API costs through optimization, and better user experiences from properly implemented AI features.
Here's where the ROI becomes obvious. Building a chatbot that doesn't hallucinate? An LLM developer implements RAG systems with proper retrieval instead of hoping the model memorized your docs. API costs eating your budget? They add caching, optimize prompts, and route simple queries to cheaper models.
Your prototype works great in demos but breaks with real users? LLM developers build error handling, rate limiting, and fallback strategies that keep things running when APIs fail or users ask unexpected questions.
Content generation features producing generic output? The right developer implements better prompts, few-shot examples, and output validation that matches your brand voice. Your competitors ship AI features that frustrate users while yours actually help.
Your job description filters candidates. Make it specific enough to attract qualified LLM developers and scare off tutorial followers.
Job Title
"Senior LLM Engineer" beats "AI Wizard" every time. Be searchable. Include seniority level since someone who played with ChatGPT last month can't architect production RAG systems yet.
Company Overview
Give real context. Your stage (seed, Series B, public). Your product (customer support automation, content generation platform, document analysis). Team size (3-person AI team vs. 20+ engineers).
Candidates decide if they want your environment. Help them self-select by being honest about what you're building. Greenfield AI features? Scaling existing systems? Mention it.
Role Description
Skip buzzwords. Describe actual work:
- "Build RAG system for customer support using internal docs and ticket history"
- "Optimize our content generation pipeline that processes 100K requests daily"
Technical Requirements
Separate must-haves from nice-to-haves. "2+ years building production LLM applications" means more than "AI experience." Your tech stack matters,OpenAI versus Anthropic versus open-source models.
Be honest about what you actually need. RAG systems? Model fine-tuning? Multi-agent orchestration? Say so upfront.
Experience Level
"4+ years backend engineering, 2+ years working with LLMs in production" sets clear expectations. Many strong developers pivoted from backend or ML roles recently. Focus on what they've shipped."
Soft Skills & Culture Fit
How does your team work? Fully remote with async communication? Role requires explaining AI limitations to non-technical stakeholders? Team values experimentation and iteration?
Skip "team player" and "excellent communication",everyone claims those. Be specific about your actual environment.
Application Process
"Send resume plus 3-4 sentences about an LLM application you built and what challenges you solved" filters better than generic applications. Set timeline expectations: "We review weekly and schedule calls within 3 days."
Good interview questions reveal production experience versus tutorial knowledge.
Strong candidates explain retrieval finding relevant context, feeding it to the LLM, and getting grounded answers. They discuss cost (fine-tuning is expensive), flexibility (RAG updates easily), and when fine-tuning actually makes sense.
Experienced developers mention prompt optimization (fewer tokens), caching common queries, routing simple questions to cheaper models, and batching when latency allows. Watch for systematic thinking about the cost/quality trade-off.
This reveals understanding of full systems. They should discuss document chunking, embedding strategies, vector database choice, retrieval methods, prompt design, and how to handle questions docs don't answer. Listen for practical considerations like cost, latency, and accuracy.
Practical candidates check retrieval quality first,are the right chunks being found? Then prompt design,does the prompt emphasize using only retrieved context? Then threshold tuning,are low-relevance chunks getting through? This shows systematic debugging.
Strong answers investigate what's slow,API latency, retrieval time, or processing? Then optimize: streaming responses for better UX, caching, faster embedding models, or parallelizing retrieval. Avoid candidates who immediately suggest "just use a faster model."
Their definition of success matters. User satisfaction? Cost efficiency? Accuracy? Strong candidates explain trade-offs they made, how they evaluated quality, and what they learned from production usage.
Experienced developers acknowledge most cases don't need fine-tuning. They discuss scenarios where it helps (style consistency, domain-specific language, reducing token usage) versus when it's overkill. This reveals understanding of trade-offs versus blindly applying techniques.
Good answers: translate technical limitations into business terms, propose alternative approaches, show examples of what's possible. They help stakeholders understand LLM capabilities instead of just saying "that won't work."
What do they focus on? Understanding user needs? Setting realistic expectations? Iterative development? Good answers mention prototyping quickly, showing what works, and adjusting based on feedback. Listen for collaborative approach.
Neither answer is wrong. But if you're scaling production systems and they only want greenfield work, that's a mismatch. Watch for self-awareness about preferences.
Strong candidates have systems,following specific researchers, reading papers selectively, experimenting with new techniques on side projects. Avoid candidates who say they read everything or don't keep up at all.
Cost to Hire LLM Developers: US vs. LATAM
Location changes your budget dramatically without affecting technical ability.
US Salary Ranges
LATAM Salary Ranges
The Bottom Line
A team of 5 mid-level LLM developers costs $700K-$950K annually in the US versus $300K-$425K from LATAM. That's $400K-$525K saved annually while getting the same technical skills, full timezone overlap, and fluent English.
These developers join your standups, debug API issues in real-time, and work your hours. The savings reflect regional cost differences, not compromised quality.
Frequently Asked Questions
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Connect with LLM Developers from Latin America in 5 days. Same expertise, full timezone overlap, 50-60% savings.
