Most agencies send you prompt engineers who can build a chatbot. We place AI Systems Engineers who architect RAG pipelines, optimize token economics, and ship autonomous agents that don't hallucinate in production.
These are the problems teams run into when they try to build AI without the right specialist.
They know LangChain basics but can't handle concurrency, rate limits, or fallback logic.
No one monitored context window usage; your AWS bill tripled in week two.
They built it, but have no framework to measure if it's actually getting better.
AI costs are harder to predict than expected.
Tightly coupled to one model provider; migration takes months when APIs change.
They can build a demo, but struggle to turn it into a reliable, scalable production system.
If you know AI can improve your product but need the right technical expertise to build it, this is for you.
You need audit trails, PII redaction, and deterministic outputs, not just "cool AI."
Your MVP works, but latency is 4s and costs are $5k/mo. You need optimization, not new features.
AI is your product. A bad hire doesn't just delay a feature; it churns out customers.
We test how developers design, build, evaluate, and scale AI systems in production.
We test chunking, hybrid search, re-ranking, and retrieval strategies—not just vector database integration.
We test hands-on experience with RAGAS, TruLens, or custom datasets to measure AI quality.
We test experience with LangSmith, Arize, tracing, and production debugging.
We test how developers use caching, model routing, and distillation to reduce inference costs.
We test how developers handle concurrency, rate limits, fallbacks, model changes, and scaling.
We test how developers identify edge cases, failure modes, and quality issues before release.
Unlike staffing agencies that rely on keyword matching, our vetting team consists of active AI practitioners. We review GitHub repos for architectural patterns, conduct live system design interviews focused on failure modes, and verify production deployments—not just LeetCode scores.
Book a Talent Call →The right AI expertise can determine your project’s reliability, cost, and speed to launch.
Our AI lead reviews your project, tech stack, and requirements before we start the search.
We identify AI engineers whose technical skills and experience match your specific needs.
Shortlisted candidates complete a relevant architecture review or code assessment before you interview.
Start with a paid micro-engagement to validate technical fit before making a full commitment.
CrecenTech is the engine that drives our success from start to finish. They communicate effectively and always look for ways to innovate and deliver better.
CrecenTech connects businesses with vetted AI developers experienced in building production-ready AI applications. We focus on technical expertise, system reliability, and matching developers to your project requirements.
CrecenTech evaluates AI developers through code reviews, technical assessments, and system design interviews. We prioritize hands-on experience with LLMs, RAG systems, AI agents, and production deployments.
CrecenTech aims to provide shortlisted AI developer candidates within 48 hours. The final hiring timeline depends on project requirements, developer availability, and interviews.
CrecenTech offers a 90-day replacement guarantee. If a developer doesn't meet expectations, we help find a replacement and coordinate knowledge transfer to minimize disruption.
An AI developer builds software applications using artificial intelligence. Their work includes AI chatbots, LLM integrations, RAG systems, AI agents, workflow automation, and intelligent business applications.
The cost of hiring an AI developer depends on experience, technical skills, project complexity, and engagement duration. Basic AI integrations generally cost less than complex AI agents or enterprise-grade AI systems.
An AI developer should understand Python, LLM APIs, prompt engineering, RAG, vector databases, and AI frameworks such as LangChain and LangGraph. Production experience with evaluation, monitoring, security, and cloud infrastructure is also important.
Yes. AI developers can integrate AI features into existing websites, SaaS products, CRMs, and enterprise software. Common integrations include AI assistants, intelligent search, document processing, and workflow automation.
An AI developer builds applications using AI models, APIs, and frameworks. A machine learning engineer focuses on training, fine-tuning, deploying, and maintaining machine learning models.
Retrieval-Augmented Generation (RAG) connects AI models to external knowledge sources, such as company documents and databases. Businesses use RAG to generate more relevant, grounded answers without retraining the underlying AI model.
Need an AI developer who can build beyond the demo? Get vetted AI talent with the skills to build, scale, and ship production-ready AI features.