The work
- Use LLM APIs and fine-tune language models for customer tasks.
- Build RAG pipelines with vector databases, chunking, embeddings and prompt management.
- Optimise inference with quantisation, vLLM, TensorRT-LLM or ONNX Runtime.
- Build API wrappers and microservices that integrate NLP models into products.
- Discuss generation-quality requirements with customers and present results.
Role-specific skills
- Commercial NLP development experience.
- Confident Python skills with Hugging Face Transformers, Datasets and Tokenizers.
- LLM fine-tuning experience with methods such as LoRA, P-tuning or QLoRA, and scalable inference.
- Understanding of transformers, attention and encoder-only/decoder-only architectures.
- RAG experience, including embedding models, ranking and post-processing.
- Knowledge of MLflow or Weights & Biases and cloud model deployment.
- Knowledge of agent frameworks such as LangChain, LlamaIndex or AutoGen.
- Experience deploying open-source LLMs on self-hosted infrastructure with vLLM or Text Generation Inference.
Commercial experience
We consider Junior+, Middle and Senior specialists. Each field requires at least six months of relevant commercial experience. Courses and personal projects can add to it.
Courses and personal projects do not replace commercial experience.
Experience using AI tools in commercial work is a plus, with results checked through tests and review and confidentiality respected.
Tell us about your experience
Describe an NLP or RAG task, the model used and how you evaluated answers. Include fine-tuning, deployment and inference optimisation experience.
Share your experienceWe connect with specialists in this field for future projects. This page is a role profile, not an open vacancy.