25 LLM Developers, From Prompt Work to Self-Hosted Models
A sharper prompt, retrieval over your documents, or a tuned or self-hosted model: we match engineers who have shipped at the depth your feature needs.
We found 25 available LLM Developers
LLM developers matched to how far into the model your feature has to go
Most companies that hire LLM developers do not need a model of their own. They need a feature whose answers hold up. Trust in that is thin: in Stack Overflow's 2025 Developer Survey, 46% of developers distrusted the accuracy of AI output, against 33% who trusted it. Cortance matches LLM engineers on the depth they have shipped, from prompts to retrieval to work on the model itself.
Prompt, retrieval or model: where the work sits
The shallowest LLM work calls a hosted model through its API. The developer writes the prompts, defines structured outputs and lets the model call your functions through tool use. The usual failure is silent: the provider updates the model, and the same prompt starts returning a format that nothing downstream checks.
One step deeper, the model answers from your own documents through retrieval-augmented generation (RAG). Here most bugs sit in retrieval, not in the model. A chunking rule that splits a table from its header returns the right document and the wrong numbers, and the answer still sounds confident.
The deepest work changes the model or runs it on your own servers: fine-tuning on your data, or an open-weight model where data may not leave your infrastructure. Our network has done this on fewer projects than the first two kinds of work, and we say so before we match.
From knowledge-base chatbots to clinical notes in Georgian
Twenty-five engineers in our network list LLM, RAG, LangChain, LangGraph or OpenAI work as of October 2026, and 17 of them are senior. Their projects cover every depth above.
Michael Y. led the RAG pipeline of MayaBot, a platform where small businesses build chatbots on OpenAI, Anthropic or Mistral models. Answers come from each client's own knowledge base, stored as pgvector embeddings in PostgreSQL, so a reply stays tied to that client's facts.
Tornike K. built Medda, which turns a recorded doctor visit into a structured note, a filled Georgian Form 100 and ICD-10 suggestions. Georgian is a low-resource language for speech models, so he wrote evals and tuned prompts against Georgian clinical text on Google Vertex AI.
At the deep end, Daniel Z. ran distributed LLM evaluation for long inference jobs at Qualia on Kubernetes, Ray and vLLM. He added checkpoints, so an interrupted evaluation resumed where it stopped, and Grafana dashboards for latency, retries and failures.
How do you know a new model or prompt made answers better?
Only a fixed test set of questions with agreed answers, rerun after every prompt or model change, shows that. Viktoriia A. built such a method on an applied research project. It combined prompt experiments across GPT, Claude and Llama, a RAG pipeline, and an LLM-as-a-Judge framework where one model grades another against written criteria.
A judge model is a shortcut, not a referee. It can share the blind spots of the model it grades, so the test set still needs answers a person signed off. Sergii K. applied the same idea to a platform of 15 coding agents. Every provider, from Claude Code and Codex to Gemini and local LLMs, sits behind one interface, and work moves on only after validation and re-testing.
When the brief is agents, or one provider's platform
Process automation and AI agents make up a rising part of the briefs we receive. A model that updates a CRM or places an order needs approval points more than retrieval tuning, and our page for AI engineers covers that work. Products built only on GPT models and the OpenAI platform have their own page for OpenAI developers. Everything between the prompt and your own model starts with one question on our side: how deep does the work go?
Most LLM engineers in our network also own the product code around the model
Of the 25 engineers with LLM work in our network, 13 work as full-stack developers and 5 as backend developers. The model call is a small part of what they write: the queue that retries it, the API that serves the answer and the screen that streams it.
That matters when the LLM feature lands inside an app you already run. Python, C# and TypeScript all appear in their LLM projects, so the engineer can work in your codebase instead of next to it.
What we ask before matching an LLM developer
- 01
Twenty questions your users ask
Send a sample of inputs and the answers you expect. They become the first test set and show how deep the work has to go.
- 02
Where the data lives
Documents, a database or a ticket history, and whether it may leave your cloud. That decides between a hosted API and your own servers.
- 03
The model you use today
Provider, monthly volume and what one answer may cost. Engineers who have switched providers or cut cost per answer move up the list.
- 04
Profiles to review
You get matched profiles with their LLM projects and decide whom to interview.
LLM work beyond chat: site answers, a business AI suite and truck loads
Site answers from two-stage retrieval
Ruslan B. built a question-answering bot for a company website: one model finds the passages, a second, fine-tuned in PyTorch, writes the answer from them.
RAG and tuned models in a business AI suite
Stanly M. added RAG with LangChain and OpenAI to Maico AI's Next.js product and tuned its models to the tasks the product serves.
Agents that find loads for a truck
Zakir A. connected CrewAI agents with custom tools to Tarmac AI's Django services, so carriers filter and order loads that fit their truck type.
Skills, model choices and warning signs in an LLM hire
Skills that go past calling an API
An LLM developer worth the rate handles structured outputs and tool calls, so the model returns data your code can parse. Retrieval skills come next: how documents are chunked, which embedding model encodes them and whether results are re-ranked before the model sees them.
Then come evaluation, cost and safety. That means a test set that gates each release, caching and smaller models for easy requests, and defenses against prompt injection wherever users or documents can write into the prompt.
When retrieval is enough and when the model has to change
Retrieval fixes missing knowledge: product data, policies, past tickets. Fine-tuning fixes behavior a prompt cannot hold, such as a strict output style or a narrow classification task, and it needs a set of good examples before it pays off.
Ruslan B. combined both on a website question-answering bot. Ivan S. fine-tuned a Phi-4 model on a service company's past estimates and ran it on-premise through ONNX. Fine-tuning shows up on fewer projects in our network than retrieval, and we tell you that when we match.
A hosted API or an open-weight model on your own servers
A hosted API costs nothing to start and charges per token, so the bill grows with every user. An open-weight model on your own GPUs costs money before the first request. In return, data stays inside your infrastructure and the price per answer stops depending on a provider's price list.
The choice turns on volume, privacy rules and the latency you can accept. Running inference with vLLM, as Daniel Z. did for evaluation at Qualia, takes its own operations work, so ask a candidate who has kept that running.
Warning signs in an LLM portfolio
Watch for a demo with no test set behind it, and for one provider's SDK called straight from business code with no layer to swap it. Other signs: "fine-tuned" used for a prompt template, and no plan for the provider rate-limiting or timing out. For interview questions and a vetting checklist, read our guide How to Hire Generative AI Developers.
Where demand for LLM work is moving
Stack Overflow's 2025 Developer Survey asked developers who use AI agents what they use them for. Language processing led at 49%, then integration with external agents and APIs at 38%, MCP servers at 34% and vector databases at 24%. The same survey found 87% of respondents concerned about the accuracy of information from AI agents.
Both numbers point to the same hiring need. Teams now ask for engineers who connect models to their data and tools, and who can prove the answers are right.
Matched on LLM projects, at $29-42 an hour
Every LLM developer comes from Cortance's own network, not resold from another company, and has passed five vetting stages, including a technical assessment and live coding.
Five vetting stages
Matched on LLM projects
Profiles within 30 minutes
Start within 2 days
$29-42 an hour
One feature, then pause
Pair Your LLM Developers
With Related Experts
Most LLM projects require additional expertise. Whether you need front-end devs, DevOps specialists, or database architects, we connect you with professionals who integrate with your LLM team.
Answers for Common LLM Hiring Questions
Can’t find what you are looking for?
LLM developer or AI engineer: which does an AI feature need?
Prompt engineer or LLM developer: who should build a production LLM feature?
Does an LLM feature need a vector database?
Is LangChain still worth using for a new LLM product?
Can an LLM app move from OpenAI to Claude or an open-weight model later?
How do LLM developers keep prompt injection and data from crossing between customers?
Can an LLM developer build for a language other than English?
Do Cortance's LLM developers overlap with US working hours?
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