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What does an LLM developer do?
An LLM developer builds product features on top of large language models, usually through a provider API or an open model the team hosts. The work is software engineering around the model: retrieval over your own data, agents that call your tools, prompt and evaluation suites, guardrails, and the cost and latency budget per request. Training a model from scratch is a different job, closer to machine learning engineering.
LLM developer, ML engineer or AI engineer: which one do you need?
Hire an LLM developer when the model already exists and the work is building a reliable product around it. Hire a machine learning engineer when you need models trained, tuned and served from your own data, such as ranking, forecasting or computer vision. "AI engineer" is the broad title that covers both. If the role mixes them, write down which half matters more, because it changes who you should interview.
- Building on existing models (RAG, agents, generative features): this page.
- Training and serving your own models, MLOps, computer vision: machine learning engineers.
- A broad AI role or AI across several areas: AI developers.
- Several people at once: AI team or data science team.
What skills should a senior LLM developer have?
A senior LLM developer is first a strong backend engineer, usually in Python or TypeScript, who can ship and run a service in production. On top of that, look for hands-on work with hosted model APIs (OpenAI, Anthropic, Google) or open-weight models, a retrieval stack with a vector index, an evaluation habit, tracing of model calls, and a clear view of security risks such as prompt injection and data leaks between users.
Tools you will often see, none of them required on their own:
- Orchestration: LangChain, LangGraph, LlamaIndex, or plain code with the provider SDK.
- Retrieval: pgvector, Pinecone, Qdrant, Weaviate or Elasticsearch, with hybrid search and rerankers.
- Evaluation and tracing: offline eval sets, LLM-as-judge checked against human labels, LangSmith, Langfuse or OpenTelemetry-based tracing.
- Open models: vLLM or SGLang for production serving, Ollama for local work, Hugging Face Transformers, quantised weights to cut cost.
A candidate who can say why they would skip a framework for a given task tells you more than a long list of them.
What is a RAG developer, and when do you need one?
A RAG developer builds retrieval-augmented generation: the system finds the relevant pieces of your documents or data and passes them to the model, so answers come from your sources instead of the model's memory. You need one when the product answers questions over a knowledge base, contracts, tickets or code. Most of the effort goes into ingestion, chunking, search quality and measuring answers, with comparatively little time on the prompt.
What does an AI agent developer build?
An AI agent developer builds systems where the model decides which step to take next and calls tools to take it: search, internal APIs, databases, code. Typical work includes support agents that act on a customer's account, internal assistants over company tools, and multi-step workflows such as document processing. The senior skill is control: step limits, typed tools, human approval for risky actions, and tracing to see what the agent did.
Do you need a generative AI developer or a prompt engineer?
For a product feature, hire a developer. Prompt writing is one part of generative AI work, and a feature also needs the code around the prompt: data access, structured outputs, retries, evaluation, monitoring and cost limits. A dedicated prompt engineer makes sense mainly for teams with a large volume of prompts and an evaluation setup already in place. For most teams we would start with one engineer who does both.
Where do NLP, fine-tuning and deep learning fit in?
NLP engineers build text classification, extraction, search and translation, and today many of those tasks run on LLMs instead of models trained per task. Fine-tuning helps a smaller model follow a fixed format, style or task at lower cost, and it needs a clean labelled dataset; it is a poor way to add facts, which is the job of retrieval. Designing or training models, including vision and speech models, belongs to machine learning engineers.
If your role is mostly fine-tuning or training, say so in the brief so the search looks for engineers with that experience. When training is most of the job, start from the machine learning engineers page instead.
How do you hire an LLM developer through Wild.Codes?
When you describe the role, you first see engineers from our ready pool who are free today and pre-selected for it, with a monthly rate on each. If none fits, confirm the role and the full search starts: our headhunter agent searches further, every engineer on the shortlist records a timed video interview with questions AI writes from your brief, AI and a human expert review it, and a talent manager signs off the shortlist.
On subscription, the first shortlist arrives within 47 hours after you confirm the role. For a permanent hire, the engine also searches the open market where the role is, and first vetted candidates arrive within 5 workdays of the search starting. In both cases you run your own interview before anyone starts. How the checks work: vetting process.
Can an LLM developer join your team full time?
Yes. On subscription the engineer works only on your product, at least 160 hours a month, inside your repositories and sprints, while we handle payroll, contracts, compliance and PTO. Rates run from $6,400 to $9,600 a month per engineer, all-in, with no placement fee. If you would rather employ the engineer yourself, a permanent hire costs 12% of annual salary, with a $2,000 minimum.
More on how a full-time engineer works inside your team: dedicated developers.
Related hiring pages
AI developers · Machine learning engineers · AI team · Data science team · Python developers · Dedicated developers · How to hire an AI developer · All hiring pages
