Hire pre-vetted GenAI Engineers

Everyone has "LLM experience" now. Most of it means they called the OpenAI API a few times and wrapped it in a Streamlit app. That's not engineering. That's a demo.

The GenAI engineers on Cortance build the infrastructure around large language models. RAG pipelines with proper chunking and retrieval. Evaluation frameworks that catch hallucinations before users do. Multi-model architectures that pick the right model for the right task. Agent systems with structured tool use, not chatbots that sound smart but break on edge cases.

Every profile on Cortance is verified through real project delivery. You can see exactly what each engineer built, which models they used, and how long they've been doing it. No inflated resumes.

Cortance has made 150+ successful placements across engineering and AI roles, with a 94% retention rate and 200% year-over-year growth since launch. The GenAI engineers in this network average 6 years of commercial experience. For a field where most practitioners started their LLM work in 2023, that foundation matters — it's what separates engineers who understand what's happening inside the stack from those who only know how to call it.

What these GenAI engineers have built

A chatbot platform (MayaBot) integrating OpenAI, Anthropic, and Mistral models with custom RAG and vector search in PostgreSQL. A GenAI Copilot for greenhouse climate management that connects to commercial controllers via REST APIs, pulls real-time sensor data, and delivers actionable recommendations through a RAG pipeline with domain-specific knowledge, reducing manual workload by 30%. LLM-as-a-Judge evaluation frameworks for automated quality scoring. An AI financial advisory agent processing 100+ parameters across 9,000 public companies, automating work previously handled by a 4-person analyst team. An automated document parsing system for a fintech company processing thousands of invoices daily, extracting structured data from PDFs using NLP and computer vision with 82% accuracy.

These aren't proof-of-concept builds. Each project listed on a Cortance profile was delivered for a real client in a production environment, with measurable outcomes attached. That's the verification standard applied before a profile goes live on this platform.

The engineering, not just the prompts

What separates a GenAI engineer from someone who uses ChatGPT well: chunking strategies that actually affect retrieval quality. Embedding model selection and fine-tuning. Hallucination reduction through grounding and verification layers. Cost control across different model tiers. Structured outputs that downstream systems can rely on. Evaluation methodology so you know when your system is getting worse, not just when it feels off.

When retrieval augmentation isn't enough, engineers on the platform have fine-tuned models using LoRA and QLoRA for domain-specific performance. For enterprise clients with strict data requirements, they've deployed open-source models on private infrastructure using vLLM and Ollama, keeping data entirely off external APIs.

The engineers here work with LangChain, LlamaIndex, LangFuse, Pinecone, pgvector, and custom orchestration layers. They build on OpenAI, Anthropic Claude, Mistral, and open-source models like Llama. They know when to use which, and more importantly, when not to use an LLM at all.

The most common pattern we see when companies come to Cortance after a failed GenAI hire: the previous engineer built something that worked in staging and broke in production when real users introduced edge cases the prompt hadn't accounted for. Robust GenAI engineering means designing for those cases from the start — with evaluation pipelines, fallback logic, and observability baked in, not added later.

How hiring works

Submit your requirements through the platform, fill a short questionnaire, or reach out to a hiring manager directly. Cortance's AI-powered matching system scores candidates against your stack, model preferences, domain, and seniority level — and delivers a curated shortlist of matched GenAI engineers within 30 minutes. Most clients complete the hire in 2 days. Cortance handles contracts, payroll, and onboarding.

How to engage

GenAI engineers on Cortance work remotely from Europe (Ukraine, Portugal, Spain, Georgia), with solid timezone overlap for EU and US East Coast teams. Engagements range from short consulting sprints to dedicated long-term roles. Every profile shows rates, project history, and exact technology experience upfront, so you know what you're getting before you reach out.

If you need to hire a generative AI engineer who builds production systems, not prototypes, browse the profiles ahead.

Frequently Asked Questions

1. Can I hire a GenAI engineer who also knows classical ML? Yes. Several engineers on the platform have 6-9 years in traditional machine learning and have added LLM, RAG, and agent engineering over the past 1-2 years. Their profiles show exact years per technology, so you can judge the depth yourself.
2. What LLM providers and frameworks do your engineers work with? OpenAI (GPT-4, GPT-4o), Anthropic (Claude), Mistral, Llama, and other open-source models. Frameworks include LangChain, LangGraph, LangFuse, and custom orchestration. Vector stores include Pinecone, pgvector, Weaviate, and ChromaDB.
3. Do you have engineers who can build AI agents, not just chatbots? Yes. Engineers on the platform have built multi-step agent systems with structured tool use, deterministic guardrails, and human-in-the-loop workflows. This goes well beyond simple conversational interfaces.
4. Can I hire a GenAI engineer for a short proof-of-concept, not a long-term role? Yes. Short consulting sprints are a common engagement model on Cortance. Several GenAI engineers specialize in scoping and validating LLM-based approaches before a company commits to a full build — which often saves significant time and budget downstream.
5. What if I need a GenAI engineer and a data engineer to work together? Cortance covers both roles. If your project requires a GenAI engineer for the model and agent layer plus a data engineer for the pipeline and infrastructure underneath, both can be matched through the same platform with the same verification standard applied to each.