Project focus Own the end-to-end cloud fine-tuning of open-source LLMs on Azure - covering both Supervised Fine-Tuning (SFT) and Reinforcement Fine-Tuning (RFT), from data preparation through training, evaluation, and deployment. The role involves working with sensitive data under strict internal access controls. Key skills and Experience required 5+ years of engineering experience, including 2+ years hands-on LLM fine-tuning Strong Python and Py Torch Production experience with Azure Machine Learning and Azure AI Foundry (managed compute, serverless fine-tuning, online endpoints, GPU quotas) Hugging Face stack (transformers, peft, trl, datasets, accelerate);




Lo RA/QLo RA and bitsandbytes 4-bit quantization SFT delivery plus working knowledge of RL/preference methods (RFT, GRPO/PPO, DPO) and reward/grader design Training-data engineering: JSONL/chat formats, curation, deduplication, split hygiene, leakage prevention Evaluation rigor: task metrics, LLM-as-judge, and reading training/validation curves Linux, Git, containers, and cloud GPU workflows Preferred: Deep Speed/multi-node training, quantization & serving (GGUF/llama.Cpp, v LLM, Ollama), Azure Open AI fine-tuning API, RAG/Graph RAG, infrastructure-as-code (Bicep/Terraform), and Responsible-AI practices #J-18808-Ljbffr

📌 Ai Engineer - Llm Fine-Tuning On Azure (España)
🏢 Darwin Recruitment
📍 España