Hire OpenAI Developers
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OpenAI Developers Ready to Start
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Builds AI-powered applications using GPT-4 and other OpenAI models for production systems. Has deployed chatbots, content generation tools, and analysis features at scale. Strong background in prompt engineering and cost optimization.
Experienced integrating OpenAI APIs into business applications. Specializes in structured outputs, function calling, and building reliable AI workflows. Has worked at SaaS companies shipping AI features to thousands of users.
Backend engineer focused on AI feature development and API architecture. Comfortable deploying OpenAI-powered systems in cloud environments. Has built intelligent automation for content, customer support, and e-commerce platforms.
Works on semantic search and recommendation systems using OpenAI embeddings. Experience with both GPT models and embedding APIs. Background in building search infrastructure for content-heavy applications.
Full-stack developer building AI features into web applications. Has shipped chatbots, writing assistants, and automated analysis tools. Works across frontend interfaces and backend OpenAI integrations.
Builds conversational AI and content generation features. Learning production patterns for prompt optimization and token management. Has worked on customer-facing AI tools and internal automation projects.
Why Hire OpenAI Developers Through Tecla?
Save 60% on Salaries
Hire senior OpenAI engineers at 40-60% less than US rates without sacrificing quality or experience level.
5-Day Average Placement
We match you with qualified OpenAI developers in 5 days on average, not the 42+ days typical with traditional recruiting firms.
Zero Timezone Hassle
Work with developers in timezones within 0-3 hours of US hours. No more waiting overnight for responses on critical AI feature issues.
97% Retention After Year One
Our placements stick. Nearly all clients keep their developers beyond the first year, proving the quality of our matches.
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 OpenAI applications.
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What Our Clients Say
"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."
"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."
"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."
The Bar We Set for All Pre-Vetted OpenAI API Developers
Building production-ready applications using GPT-4, GPT-3.5, and other OpenAI models. Our OpenAI API developers work with function calling, structured outputs, and streaming responses to deliver AI features that handle real user needs reliably.
Expert-level experience with prompt design, few-shot learning, chain-of-thought reasoning, and output formatting. They craft prompts that consistently produce accurate results while minimizing token usage and API costs.
Deep expertise in connecting OpenAI APIs to databases, external tools, and business systems. Plus advanced knowledge of error handling, rate limiting, and fallback strategies to keep AI features stable in production.
Our OpenAI developers proactively monitor token usage, implement caching strategies, choose appropriate models for each task, and optimize response times. They also provide guidance to ensure your AI features scale affordably as usage grows.
Hire OpenAI Developers in 4 Simple Steps
Describe Your Requirements
Evaluate Pre-Screened Candidates
Interview Potential Hires
Onboard and Deploy
Our Hiring Models
Select the option that matches your needs.
True Cost to Hire AI Agent Developers: LATAM vs. US
OpenAI developers command premium rates in US markets due to high demand for AI integration skills. Location changes your total hiring investment significantly. US full-time hires carry overhead beyond base salary: health benefits, payroll taxes, recruiting fees, and administrative costs.
US Full-Time Hiring: Hidden Costs
Senior OpenAI developers in major US tech hubs run $175K-$235K base. The all-in cost is substantially higher.
- Health insurance: $12K-$17K
- Retirement contributions: $10.5K-$14.1K (401k matching, ~6% of base)
- Payroll taxes: $14K-$18.8K (FICA, unemployment, ~8% of base)
- PTO: $8.8K-$11.8K (accrued time off, ~5% of base)
- Administrative costs: $6K-$9K (HR, payroll processing)
- Recruitment costs: $26.3K-$35.3K (agency fees, ~15% of base)
Total hidden costs: $77.6K-$105.9K per developer
Adding base compensation brings total annual investment to $252.6K-$340.9K per OpenAI developer.
LATAM Hiring Through Tecla
All-inclusive rate: $103K-$138K
This covers compensation, local benefits, payroll taxes, PTO, HR administration, recruiting, technical vetting, legal compliance, and performance management. No hidden fees, no agency markup, no administrative burden. Your OpenAI developer joins your Slack, attends standups, and ships AI features while you focus on product strategy.
The Real Savings
US total cost for a senior OpenAI developer runs $252.6K-$340.9K annually when factoring in all overhead. Tecla's all-inclusive rate: $103K-$138K. You save $114.6K-$202.9K per developer (45-60% reduction).
A team of 5 OpenAI developers costs $1.3M-$1.7M annually in the US. Through Tecla: $515K-$690K. Annual savings: $748K-$1.01M. Same technical capability with GPT-4 and embeddings, English fluency for architecture discussions, timezone alignment for real-time debugging.
Resources can be replaced at no cost during the 90-day trial. No recruiting fees or placement costs. Transparent all-inclusive pricing from month one.
What is an OpenAI Developer?
OpenAI developers build applications powered by GPT models and other OpenAI APIs. They create chatbots, content generation tools, analysis systems, and intelligent automation that integrates AI into business workflows. They architect solutions that balance functionality with cost and reliability.
OpenAI developers sit between application development and AI engineering. They're not ML researchers training models, but they understand LLMs well enough to build reliable applications around them. Most work involves prompt engineering, API integration, and designing systems that use AI effectively.
They differentiate from general backend developers through deep knowledge of prompt design, token management, and how to structure applications so AI features work predictably. Unlike data scientists, they ship customer-facing products instead of experimental notebooks.
Companies hire OpenAI developers when moving beyond ChatGPT experiments into production AI features. This happens after deciding an AI-powered feature makes business sense but before knowing how to make it reliable, cost-effective, and fast enough for real users.
Business Impact
When you hire an OpenAI developer, AI features stop being demos and start handling real traffic. Most companies see faster iteration on AI applications and more predictable costs.
Prototype to Production: Turn working demos into reliable features that handle edge cases, manage errors gracefully, and don't break when the API returns unexpected responses.
Cost Management: Token usage drops 40-70% while maintaining output quality through prompt optimization, model selection, and caching. Features that were burning $12K/month become sustainable.
User Experience: Focus on latency and reliability delivers responses in under 2 seconds instead of making users wait 15 seconds. Features that actually work when users need them.
Your job description filters for OpenAI API developers who've shipped AI features, not completed tutorials. Make it specific enough to attract people who've debugged production prompt failures.
What Role You're Actually Filling
State whether you need someone to build chatbots, create content generation tools, develop analysis features, or own your AI strategy. Include what success looks like: "Shipping a writing assistant that 80% of users engage with daily" beats "building AI solutions."
Give context about your current implementation, use cases, and what's not working. Are you getting inconsistent outputs? Burning through your API budget? Help candidates understand if this matches problems they've solved.
Must-Haves vs Nice-to-Haves
List 3-5 must-haves that truly disqualify. "Built production OpenAI applications handling 5K+ daily users" is specific. "Experience with AI" is worthless. Include years with specific APIs (GPT-4, embeddings, function calling) and outcomes (improved accuracy, reduced costs).
Separate required from preferred so strong candidates don't rule themselves out. Experience with fine-tuning might be nice, but if someone's built reliable GPT-powered features and can learn it, don't lose them.
How to Apply
Tell candidates to send a brief description of the most complex OpenAI application they built and what broke in production. This filters for people who've shipped real features.
Set timeline expectations: "We'll respond within 5 business days and schedule first interviews within 2 weeks" beats radio silence.
Good questions reveal how candidates think about prompt engineering, cost management, and production reliability. Not surface-level knowledge.
What it reveals: Understanding of prompt design, structured outputs, and validation strategies. Listen for specific decisions about model selection, few-shot examples, and how they'd measure output quality.
What it reveals: Hands-on cost management beyond "use GPT-3.5 instead of GPT-4." Look for prompt compression, caching strategies, when to use different models, measuring quality versus cost.
What it reveals: Whether they own outcomes or execute tasks. Listen for ownership of metrics like output quality, latency, cost per request. Strong candidates explain edge cases and monitoring.
What it reveals: How they debug under uncertainty and learn from failures. Look for honesty about what went wrong, specific debugging techniques, and safeguards added.
What it reveals: Strategic thinking about cost-quality trade-offs. Watch for frameworks around when quality justifies premium models versus when good-enough works.
Collaborative problem-solving and communication style. Listen for partnership mindset, not gatekeeping. Strong candidates educate stakeholders and help teams make informed decisions.
What it reveals: Honest self-assessment about what energizes them. Neither answer is wrong, but helps identify mismatches. Strong candidates know what they're good at and what drains them.
Frequently Asked Questions
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