Hire LLM engineers for RAG, fine tuning and evals
Hire Caudify's LLM engineers when the hard part is the model layer: getting retrieval right, choosing between models, fine tuning for a narrow task, hosting open-weight models privately, or proving quality with evals. They work alongside your product team, or as a dedicated pod, and leave behind the eval sets and pipelines your team can keep running.
- Remote, worldwide
- Embedded, pod or project
- You own the code
- Human review on every change
What Caudify's LLM engineers build for clients.
RAG pipelines
Ingestion, chunking, hybrid search, reranking and permission-aware retrieval, with cited answers.
Model selection and routing
Benchmarks on your task, then routing between models by cost and difficulty.
Fine-tuned models
Supervised fine tuning and LoRA for narrow formats and behaviours, with before and after evals.
Private LLM hosting
Open-weight models served in your cloud, with quantisation, monitoring and access control.
Skills and tools LLM engineers bring.
Retrieval
- Embeddings and chunking strategy
- Hybrid BM25 + vector search
- Reranking
- pgvector and vector stores
- Retrieval metrics
Models
- Frontier APIs and open-weight models
- SFT, LoRA / QLoRA
- Structured outputs
- Quantisation and serving
- Cost and latency profiling
Evaluation
- Golden datasets from real data
- LLM-as-judge with calibration
- Faithfulness and relevance scoring
- Regression evals in CI
How to judge LLM engineers, wherever you hire them.
Useful whether you hire from Caudify, a freelancer platform or your own pipeline. The full checklist is in how to hire AI developers.
What good looks like
- They debug retrieval before touching prompts or models.
- They can explain when fine tuning helps and when retrieval or better prompts are the cheaper fix.
- They treat LLM-as-judge scores as a tool to be checked against human labels, not as ground truth.
- They know the security risks specific to LLM apps, such as prompt injection and excessive agency.
Questions worth asking
How to hire LLM engineers from Caudify.
The same three models apply to every role. Compare them with freelancers and in-house hiring.
Embedded engineers
Caudify engineers join your standups, repositories and tools, and work from your backlog under your technical lead.
- Best when
- You have a product team and need AI depth inside it.
- You bring
- Direction and priorities
- Caudify brings
- Senior AI engineering, code review, Caudilysis scans on our changes
Dedicated AI pod
A small cross-functional Caudify team owns an AI product area end to end: discovery, build, evals, release and iteration.
- Best when
- You need a whole AI capability built and run, not extra hands.
- You bring
- Goals and decisions
- Caudify brings
- Delivery, quality, evals, hosting and reporting
Scoped project
A defined scope, milestones and acceptance criteria agreed up front, delivered and handed over with documentation.
- Best when
- The outcome is clear: a RAG assistant, an agent, an integration.
- You bring
- Acceptance and feedback
- Caudify brings
- The finished, tested, documented system
Prefer a defined outcome?
The same engineers deliver these as scoped services.
Need a different mix?
Reading for teams hiring LLM engineers.
- AI · 6 min readRAG vs fine-tuning: how to choose for your LLM applicationRAG gives a model the right knowledge at the moment it answers. Fine-tuning changes how the model behaves. Most teams need the first, some need both, and the way to decide is an evaluation set, not a debate.
- Engineering · 7 min readHow to hire AI developers: skills, interview questions and red flagsMost AI products are mostly ordinary software with a model in the middle. Hire for evaluation discipline and production experience first, model trivia last. Here is the checklist we use.
- AI · 5 min readAI-assisted software development: how we use LLMs from discovery to releaseAI makes the mechanical parts of software delivery faster. It doesn't make decisions, and it doesn't skip review. Here is where LLMs sit in each stage of our delivery, and the rules that keep them honest.
Hiring LLM engineers, answered.
What is an LLM engineer?
An LLM engineer specialises in building with large language models: retrieval augmented generation, prompt and output design, model selection, fine tuning, hosting and evaluation. Where an AI developer covers the whole product, an LLM engineer goes deep on the model layer.
Do we need an LLM engineer to fine tune a model?
Not always. Many teams fine tune too early. An LLM engineer's first job is to measure whether prompting and retrieval already meet the bar; fine tuning is worth it when evals show a gap they can't close.
Can your LLM engineers host models privately?
Yes. They can deploy open-weight models in your cloud account or on dedicated hardware, so prompts and data stay inside your environment.
How much does it cost to hire an AI developer from Caudify?
It depends on the engagement model, the skills involved and how long you need them, so we quote per scope after a free scoping conversation. We don't publish a rate card, because a RAG prototype, an embedded senior engineer and a dedicated pod are priced very differently.
Tell us what you're building.
A free assessment is the start of every engagement. We look at the product, the data and the constraints, then come back with an honest view of whether hiring LLM engineers is the right move and how we would staff it.
Looking for a job instead? See open roles at Caudify.