AI-Readiness
Not which LLM they know. How they think about and use AI across the full stack.
Hire LLM Developers
When only 3% of LLM developers make it through our vetting, you spend less time screening and more time shipping. No tutorial followers, no resume padding; just engineers who've built production AI before and can do it again for you.
- 90-day Replacement Guarantee
- 5-Day Average Placement
Tecla: The AI talent partner for Engineering teams
Every candidate goes through a four-part assessment covering AI-readiness, technical depth, soft skills, and English fluency. AI-readiness means how an engineer thinks about and uses AI across the full stack. Tooling choices, architectural decisions, problem-solving under pressure. We evaluate the whole picture.
Technical Depth
Assessed by our engineering team, not a recruiter with a keyword checklist.
Soft Skills
Communication, collaboration, and how they show up on a cross-functional team.
English Fluency
Evaluated in real technical conversations, not a multiple choice test.
Tecla is not a generalist staffing agency that added an AI filter. We are an AI-specialist talent network.
What our LLM Engineers build for you
LLM Application Development
Production LLM applications using OpenAI, Anthropic, and open-source models. Chatbots, document analysis, and content generation built with LangChain, RAG, and vector databases.
RAG Systems & Vector Search
RAG systems using Pinecone, Weaviate, or Chroma. Chunking strategies, embedding optimization, and hybrid search that actually return accurate results in production.
Prompt Engineering & Optimization
Better prompts mean lower API costs and higher output quality. They design few-shot and chain-of-thought systems, implement caching, and build evaluation frameworks so quality does not degrade over time.
Model Fine-tuning & Deployment
When off-the-shelf models are not enough, they fine-tune on your domain-specific data. Training pipelines, evaluation metrics, deployment infrastructure, and the judgment to know when a better prompt solves it instead.
Ready to hire faster?
LLM developers ready to join your team
These are representative profiles from our active network. Request your shortlist and we'll match you with engineers fit for your specific stack.
Sophia MitchellSenior AI Engineer
United States
9 years experienceBuilt and scaled AI-powered data pipelines and LLM applications for Fortune 100 companies. Specializes in multi-agent systems, vector search, and MLOps. Led a team of 8 engineers to ship a production AI assistant used by 1M+ enterprise users.
Skills- James CarterPrincipal LLM EngineerUnited States11 years experience
Architected and deployed large-scale LLM solutions for enterprise clients across fintech and healthcare sectors. Deep expertise in OpenAI and Anthropic APIs, with a track record of reducing hallucination rates through advanced prompt engineering and retrieval-augmented generation.
Skills - Isabella TorresSenior AI Solutions ArchitectBrazil10 years experience
Designed enterprise AI systems for Fortune 500 clients. Specializes in multi-agent architectures and complex workflow automation. Led migrations from GPT-3.5 to GPT-4 at scale.
Skills - Roberto MartinezSenior AI Backend EngineerCosta Rica8 years experience
Architected scalable LLM backends handling 10M+ requests daily. Expert in prompt caching, rate limiting, and cost optimization. Implemented robust error handling for API failures.
Skills - Camila SantosSenior AI Product EngineerChile6 years experience
Built full-stack AI applications from prototype to production. Specializes in RAG systems and conversational interfaces. Strong collaboration with product teams on AI features.
Skills - Luis HernandezSenior ML EngineerMexico8 years experience
Fine-tuned domain-specific models for legal and healthcare applications. Deep expertise in model evaluation and deployment pipelines. Cut inference latency by 70% through optimization.
Skills - Patricia RamosLead AI EngineerArgentina9 years experience
Designed LLM-powered applications processing 2M+ API calls monthly. Expert in fine-tuning, embeddings, and context optimization. Reduced API costs by 60% through caching strategies.
Skills - Diego AlvarezSenior LLM EngineerColombia7 years experience
Built production chatbots serving 500K+ users for SaaS platforms. Specializes in RAG architectures and prompt engineering at scale. Previously led AI features at a Series B startup.
Skills
Why hire LLM Developers through Tecla?
5-Day Average Placement
We match you with qualified LLM developers in 5 days on average. Traditional recruiting firms take 42+ days, and that's before the notice period.
Top 3% Acceptance Rate
Only 3 in 100 applicants make it through our vetting process. Every developer you meet has already proven themselves building production LLM applications, not just completing side projects.
The talent is there. You decide where they are based
Tecla places senior LLM engineers in the US and Latin America. Go US-based when that is what the role needs. Go nearshore when you want to reinvest the savings into shipping faster. Same expertise, your call.
Stop rehiring the same role every 18 months
Our placements stick. Nearly all clients keep their developers beyond the first year, proving the quality of our matches.
Zero Timezone Hassle
Full overlap with US business hours. No more waiting overnight for responses or debugging production issues alone at midnight.
Hire LLM Developers in 4 simple steps
Tell us what you need
Share your tech stack, seniority level, and what you're building. No lengthy forms. No back-and-forth for days. One focused call and we handle the rest.
Receive your shortlist within 3–5 days
Every profile includes verified production experience, not self-reported skills. You're reviewing engineers who have shipped real LLM features, not completed tutorials.
Interview your top choices
See how they think through problems and explain technical decisions. You're evaluating fit, not teaching fundamentals. Candidates arrive briefed on your product context.
Start working together in week 2–3
We handle contracts, compliance, and paperwork across borders. You focus on onboarding them to your codebase and product goals.
90-day replacement guarantee. If the match isn't right, we find you another, at no extra cost.
Teams building with AI trust Tecla to hire
Eleven years, 500+ companies, 50,000+ vetted professionals. What they say about working with us.
Tecla is organized and provides a strong partnership experience. From hiring multiple engineers within weeks to maintaining consistent communication and feedback, they've shown real professionalism. Their follow-up and collaboration made the entire staffing process efficient and enjoyable.
Kristen MarcoeDirector @ Credo AI
I’m very happy with Tecla. Their support has improved our QA process, reduced bug reports by half, and made our onboarding process twice as fast. The team is responsive, cost-effective, and delivers high-quality candidates on time. Tecla has truly become a trusted extension of our internal hiring team.
Meit ShahPrincipal PM @ Stash
It was a pleasure working with Tecla. Their team quickly understood our hiring needs and found candidates that matched our technical requirements perfectly. Communication was seamless, and they were always quick to respond and deliver results. Tecla’s attention to quality made the entire experience smooth and efficient.
Mayya BozhilovaManager @ Three Space Lab
Tecla successfully found candidates for our team and handled the entire process from scheduling to interviews. They were timely, responsive, and always kept communication flowing through email and messaging apps. I was really impressed with Tecla’s follow-up and thoroughness throughout the process.
Jessica WarrenHead of People @ Chowly
Tecla's business model and team set our company up with engineers that we have the real possibility of working with long-term and can grow with our business. Tecla came in highly recommended, and their pace from introduction to engagement to presenting candidates was very fast.
David BradleyFounder @ QPilot
Internally, we're moving much faster than we were without the remote engineers Tecla recruited for us and we've been able to implement far more features. Once we brought on our first full-time designer in South America, it made the quality of our user interface, product, and marketing efforts increase substantially.
Drew BatshawCTO @ Waggl
When we started our recruiting initiatives for LATAM developers, it was crucial for us to rely on a company that could provide deep local expertise to help us identify the best software developers in Latin America. The teams at Tecla really go the extra mile to understand our needs, which is what has made our partnership so successful!
Douglas SantosLead Tech Recruiter @ HomeLight
The real cost to hire LLM Developers with Tecla
US Salary Ranges
- Junior
- $100,000-$140,000 annually
- Mid-level
- $140,000-$190,000 annually
- Senior
- $190,000-$260,000+ annually
LATAM Salary Ranges
- Junior
- $45,000–$60,000 annually (55–57% savings)
- Mid-level
- $60,000–$85,000 annually (52–55% savings)
- Senior
- $80,000–$115,000 annually (50–58% savings)
Tecla places LLM developers across the US and Latin America. Same production experience, same timezone overlap, same fluent English. You choose where they are based.
What is an LLM Developer?
An LLM developer is the engineer who makes AI actually work in your product. They take foundation models like GPT-4, Claude, or Llama and build reliable, cost-efficient systems around them: prompt engineering, RAG architectures, API optimization, and the backend infrastructure to run it all at scale. Not a researcher. Not someone who completed a course. The person you hire when you need AI shipped.
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.
Technical Depth
Explain how RAG systems work and why they're better than fine-tuning for most use cases.
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.
How would you reduce API costs for an LLM application processing 1M requests monthly?
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.
Design a chatbot that answers questions using company documentation. Walk me through your architecture.
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.
Problem-Solving
Your RAG chatbot keeps hallucinating information that's not in your documents. How do you debug this?
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.
Users complain your AI feature is slow. Response times hit 10-15 seconds. What's your approach?
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."
Experience & Judgment
Tell me about an LLM application you built. What worked well and what would you change?
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.
When does fine-tuning make sense versus better prompts and RAG?
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.
Collaboration
How do you explain to non-technical stakeholders why an AI feature can't do something they want?
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."
Describe working with a product manager on an AI feature. How did you scope it?
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.
Cultural Fit
Do you prefer building new AI features from scratch or optimizing existing systems?
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.
How do you stay current with LLM developments when the field moves fast?
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.
Frequently asked questions about hiring LLM Developers
How much does it cost to hire LLM developers through Tecla?
Tecla has LLM developers in the US and Latin America. Hire US-based and pay US market rates. Go nearshore and pay $45K to $115K per year for the same production experience, same tools, same frameworks. Same skill either way. You choose what fits your team and budget.
How does Tecla's process work to hire LLM developers?
Post your 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.
We maintain a vetted pool of 50,000+ developers. No sourcing delays or screening candidates who just played with ChatGPT. 90-day guarantee ensures technical fit.
Do your vetted LLM developers have the same skills as US-based engineers?
Yes. Our developers work with OpenAI, Anthropic, LangChain, and vector databases, identical tech to US-based engineers. 80%+ are fluent in English. Many have worked with US companies remotely for years. Cost reflects regional economics, not skill gaps.
What hidden costs should I consider when I hire LLM developers?
US hiring includes 25-35% benefits overhead, 20-25% recruiting fees, onboarding costs, office overhead, and turnover risk (6-9 months salary).
Nearshore through Tecla eliminates most of these. Developers handle local benefits, recruiting is pre-vetted with transparent rates, remote setup costs less, and 97% retention prevents constant rehiring.
How quickly can I hire LLM developers through Tecla?
Traditional: 8-16 weeks (sourcing, screening, interviews, negotiation, notice period). Tecla: 2-3 weeks total.
You hire 6-13 weeks faster. While competitors spend months filling roles, you're onboarding someone who starts building AI features next week.
What happens if the developer isn't a good fit?
Every placement comes with a 90-day replacement guarantee. If the match isn't working, we find you another developer at no additional cost.
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Ready to hire LLM developers?
No commitment. No lengthy intake forms. A 30-minute call, a shortlist in 5 days, and a 90-day guarantee if the fit isn't right.