AI-Readiness
Not a framework checklist. How they think about and use AI end to end.

Hiring prompt engineers is hard enough without the risk of it not working out. Tecla matches you in 5 days and covers you for 90, so the only thing you have to focus on is building.
Getting into Tecla's network means clearing four bars: AI-readiness, technical depth, soft skills, and English fluency. AI-readiness measures how an engineer thinks about and applies AI across their work, from tooling decisions to architectural choices to how they approach complex problems. We evaluate everything before you meet anyone.
Not a framework checklist. How they think about and use AI end to end.
Assessed by engineers who have built the same things your team is building.
Evaluated for how they show up in standups, reviews, and cross-team work.
Tested in live technical conversation, not a form.
Generalist agencies pivoted to AI. Tecla was built for it.
Prompts that work in production, not just in testing. Few-shot learning, chain-of-thought reasoning, and structured output formats built to handle edge cases, maintain brand voice, and stay within token budgets.
Evaluation datasets, automated testing, A/B experiments, and quality metric tracking. Frameworks that catch regressions before users see them, not after.
Prompt length optimized, caching implemented, simpler tasks routed to cheaper models, requests batched intelligently. Your monthly API bill drops while performance stays strong.
Context management, relevance filtering, and citation formatting designed to ground answers in your documentation. Prompts that retrieve accurately, not confidently hallucinate.
These are representative profiles from our active network. Request your shortlist and we will match you with engineers fit for your models, use cases, and product stage.
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Full overlap with US business hours. No more waiting overnight for responses or fixing Spark jobs alone at midnight while your data team waits for their morning reports.
Only 3 in 100 applicants make it through our vetting process. Every developer you meet has built production NLP systems with real accuracy requirements, not just completed a Hugging Face tutorial.
We match you with qualified Prompt Engineers in 5 days on average. Traditional recruiting firms take 42+ days and that is before the notice period.
Tecla places senior Prompt 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.
A Prompt Engineer who understands your lakehouse architecture, Delta tables, and cluster configs gets more valuable over time. Our 97% year-one retention means that investment stays on your team.

Eleven years, 500+ companies, 50,000+ vetted professionals. What they say about working with us.

Share your models, use cases, and product stage. No lengthy forms. No back-and-forth for days. One focused call and we handle the rest.

Every profile includes verified production experience, not self-reported skills. You are reviewing engineers who have shipped real AI features, not people who experimented with ChatGPT.

See how they approach prompt design, handle ambiguous requirements, and think about evaluation and iteration. You are assessing fit, not teaching fundamentals. Candidates arrive briefed on your product context.

We handle contracts, compliance, and paperwork across borders. You focus on aligning them with your product goals, brand voice, and model stack.
90-day replacement guarantee. If the match is not right, we find you another at no extra cost.
A prompt engineer is the person who makes your AI features work with real users, not just in demos. They design the instructions, examples, and constraints that get foundation models like GPT-4 and Claude to produce consistent, on-brand, cost-efficient outputs at scale. The person you hire when your AI outputs need to be reliable enough to put in front of customers.
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.
"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.
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.
Skip buzzwords. Describe actual work:
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.
"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.
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.
"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.
Whether you hire in the US or Latin America, Tecla's Prompt Engineers bring the same production depth, the same overlap with your hours, and the same fluency. You choose where they are based.
Tecla has Prompt Engineers 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. You choose what fits your team and budget.
Post requirements on day one. Review pre-vetted candidates in days 2 to 5. Interview matches in week one to two. Hire and onboard in week two to three. Total: 2 to 3 weeks versus 6 to 12 weeks traditionally. We maintain a vetted pool of 50,000+ developers, so there are no sourcing delays and no reviewing candidates who discovered prompt engineering last month.
Yes. Our engineers work with OpenAI, Anthropic, and open-source models. They have built production prompt systems, evaluation frameworks, and RAG integrations. 80%+ are fluent in English. The cost difference reflects regional economics, not a gap in capability.
Traditional recruiting takes 8 to 16 weeks from job post to start date. Through Tecla: 2 to 3 weeks total. You hire 6 to 13 weeks faster. While other teams are still sourcing, you are onboarding a prompt engineer who starts improving your AI outputs this week.
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 engineer at no additional cost.
No commitment. A 30-minute call and a shortlist in 5 days. 90-day guarantee if the fit is not right.