Hire Prompt Engineers
Build your AI team with nearshore prompt engineers from Latin America who turn generic model outputs into production-ready features. Start interviewing in 5 days while saving 40-60% compared to US hiring, with zero compromise on quality or collaboration.
Senior Prompt Engineers Ready to Join Your Team
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Designed prompt strategies for enterprise clients across finance and e-commerce. Specializes in brand voice consistency and compliance requirements. Led prompt optimization projects reducing costs by $40K+ monthly.
Why Hire a Prompt Engineer Through Tecla?
Zero Timezone Hassle
Your prompt engineers work within 0-3 hours of US time. Debug production issues during your workday, iterate on prompts in real-time, and ship AI features without overnight delays.
Top 3% Acceptance Rate
We accept 3 out of every 100 applicants. You get prompt engineers who've shipped production AI features handling real user traffic, not people who just played with ChatGPT.
5-Day Average Placement
Match with qualified candidates in 5 days on average versus 42+ days with traditional recruiting. Start interviewing this week instead of next quarter.
Save 60% on Salaries
Senior prompt engineers from LATAM cost 40-60% less than US rates. Same expertise in GPT-4, Claude, and LangChain, lower cost of living, not lower skill.
97% Retention After Year One
Our placements stick around. Nearly all clients keep their prompt engineers past the first year, proving quality matches and cultural fit.
What Our Clients Say
The Standards Every Tecla Prompt Engineer Meets
Hire AI Prompt Engineers in 4 Simple Steps
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What is a Prompt Engineer?
An AI prompt engineer designs the instructions that make LLMs produce useful outputs. Think of them as the bridge between what you want AI to do and getting the model to actually do it reliably, not just once, but consistently in production.
The difference from regular AI engineers? Prompt engineers focus on the human-model interface. They understand how models interpret instructions, what examples help, how to structure context, and which techniques work for different tasks. Less about training models, more about making existing models perform specific jobs well.
These folks sit at the intersection of product design, copywriting, and technical engineering. They're not just writing prompts, they're building systems that test prompts, measure quality, optimize costs, and maintain consistency as products scale.
Companies hire AI prompt engineers when they're building AI features, scaling from prototype to production, or finding that generic ChatGPT outputs don't cut it for real products. The role emerged when companies realized foundation models need careful prompting to work reliably.
When you hire prompt engineers, your AI features go from "works in demos" to "works with real users." Most companies see output quality improve 30-50%, API costs drop 40-60% through optimization, and fewer edge cases breaking the user experience.
Here's where the ROI shows up. Building a customer support chatbot? A prompt engineer designs responses that match your brand voice and actually resolve issues instead of frustrating users. Content generation producing generic fluff? They craft prompts with examples that capture your style and deliver useful output.
Your AI features work great with test data but fail with real users? Prompt engineers build evaluation frameworks that catch problems before launch. They test edge cases, handle ambiguous inputs, and design fallbacks when models produce nonsense.
API bills climbing as usage grows? Good AI prompt engineers optimize token usage, implement caching for common queries, and route simple tasks to cheaper models. Your costs scale slower than your user base.
Your job description filters candidates. Make it specific enough to attract qualified prompt engineers and scare off people who just discovered ChatGPT last month.
Job Title
"Senior Prompt Engineer" or "AI Product Engineer" beats "AI Wizard." Be searchable. Include seniority level since someone who's experimented with prompts can't design production systems with evaluation frameworks yet.
Company Overview
Give real context. Your stage (seed, Series B, public). Your product (customer support automation, content generation, document extraction). What models you use (OpenAI, Anthropic, open-source). Team size (solo AI hire vs. 10-person ML team).
Candidates decide if they want your environment. Help them self-select by being honest about what you're building.
Role Description
Skip buzzwords. Describe actual work:
- "Design prompts for customer support chatbot handling 10K conversations daily"
- "Optimize our content generation system to maintain brand voice while cutting API costs"
Technical Requirements
Separate must-haves from nice-to-haves. "2+ years building production prompt systems" means more than "AI experience." Your tech stack matters, GPT-4 versus Claude, LangChain versus custom code, RAG systems.
Be honest about what you need. Few-shot learning expertise? Evaluation framework experience? Multi-turn conversation design? Say so upfront.
Experience Level
"3+ years in product or engineering roles, 2+ years specifically with prompt engineering in production" sets clear expectations. Many strong prompt engineers came from copywriting, product, or software backgrounds. Focus on what they've shipped.
Soft Skills & Culture Fit
How does your team work? Fully remote with async? Role requires collaborating with product designers on AI UX? Team values systematic testing and iteration?
Skip "team player" and "creative thinker", everyone claims those. Be specific about your actual environment.
Application Process
"Send resume plus a prompt you designed for a real product and what made it effective" filters better than generic applications. Set timeline expectations: "We review weekly and schedule calls within 3 days."
Good interview questions reveal production experience versus casual experimentation.
Strong candidates discuss understanding the task deeply first, creating evaluation criteria, starting with simple prompts, testing with edge cases, iterating based on failures, and implementing few-shot examples. They should mention measuring quality systematically, not just eyeballing outputs.
Experienced prompt engineers discuss identifying failure patterns, adding specific instructions for those cases, using conditional logic in prompt chains, implementing validation and retry strategies, or routing edge cases to different models. Watch for systematic debugging approach.
This reveals depth of understanding. They should explain few-shot provides examples to guide output format and style, discuss trade-offs (token usage versus consistency), and mention scenarios where each works best. Listen for practical experience, not textbook definitions.
Practical candidates check which prompts use the most tokens, analyze if outputs are unnecessarily verbose, look for redundant API calls that could be cached, and consider routing simple queries to cheaper models. This shows cost-conscious thinking.
Strong answers investigate what types of questions trigger bad answers, check if the model is hallucinating versus retrieval problems in RAG systems, review prompt instructions for ambiguity, and implement better output validation. Avoid candidates who blame the model without checking their prompts first.
Their definition of effective matters. Consistency? Quality? Cost efficiency? Strong candidates explain the problem it solved, iterations they went through, how they tested it, and what metrics improved. Vague answers about "really good outputs" signal thin experience.
Experienced prompt engineers acknowledge most cases don't need fine-tuning. They discuss scenarios where it helps (consistent style, domain-specific language, extreme cost sensitivity) versus when better prompts solve the problem. This reveals understanding of trade-offs.
Good answers: show what's possible with quick prototypes, explain limitations through examples not lectures, propose alternatives when requests aren't feasible, and iterate based on user feedback. They help teams understand AI capabilities without gatekeeping.
What do they focus on? Handling API failures? Managing rate limits? Parsing structured outputs reliably? Good answers mention technical constraints they hadn't considered and how they adapted prompts. Listen for collaborative mindset.
Neither answer is wrong. But if you're optimizing production systems and they only want greenfield work, that's a mismatch. Watch for self-awareness about preferences and work style.
Strong candidates discuss starting with working prompts that solve the core problem, measuring quality to know when good enough beats perfect, and knowing when technical debt in prompts becomes worth addressing. Avoid candidates who never ship or never refactor.
Cost to Hire Prompt Engineers: LATAM vs. US
Location changes your budget dramatically without affecting technical ability.
US Salary Ranges
LATAM Salary Ranges
The Bottom Line
A team of 5 mid-level prompt engineers costs $600K-$850K annually in the US versus $275K-$400K from LATAM. That's $325K-$450K saved annually while getting the same expertise in GPT-4, Claude, and LangChain.These LatAm prompt engineers join your product reviews, iterate on AI features in real-time, and work your hours. The savings reflect regional cost differences, not compromised quality.
Frequently Asked Questions
Ready to Hire Prompt Engineers?
Connect with Prompt Engineers from Latin America in 5 days. Same expertise, full timezone overlap, 50-60% savings.
