6 of 6 SpaCy Jobs in London

with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Proven experience developing applications leveraging state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine-tuning, and evaluation. Strong grasp of advanced More ❯

with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes). Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Proven experience developing applications leveraging state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine-tuning, and evaluation. Strong grasp of advanced More ❯

development of innovative solutions. Your Profile Essential skills/knowledge/experience: Proficiency in Python and extensive experience with AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience with state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks More ❯

development of innovative solutions. Your Profile Essential skills/knowledge/experience: Proficiency in Python and extensive experience with AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). Experience with state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude), including prompt engineering, fine-tuning, and evaluation. Hands-on experience with core GenAI frameworks More ❯

understanding of neural networks, CNNs, RNNs, LSTMs, and transformers Experience building automated data pipelines Hands-on experience with LLM tooling and libraries (e.g., Hugging Face Transformers/PEFT, tokenisers, spaCy or similar) Experience shipping NLP systems: prompt engineering, fine-tuning (e.g., LoRA/PEFT), vector search, and RAG-based services Great knowledge of NLP algorithms: tokenisation, embeddings, attention, language modelling More ❯

understanding of neural networks, CNNs, RNNs, LSTMs, and transformers Experience building automated data pipelines Hands-on experience with LLM tooling and libraries (e.g., Hugging Face Transformers/PEFT, tokenisers, spaCy or similar) Experience shipping NLP systems: prompt engineering, fine-tuning (e.g., LoRA/PEFT), vector search, and RAG-based services Great knowledge of NLP algorithms: tokenisation, embeddings, attention, language modelling More ❯