8 of 8 SpaCy Jobs in London

innovation, creating reusable frameworks, prototypes, and thought leadership What we’re looking for: ● Strong expertise in Python and leading AI/ML/NLP libraries (e.g. Hugging Face Transformers, spaCy, NLTK) ● Hands-on experience with LLMs (GPT, Claude, Llama, Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi More ❯

innovation, creating reusable frameworks, prototypes, and thought leadership What we’re looking for: ● Strong expertise in Python and leading AI/ML/NLP libraries (e.g. Hugging Face Transformers, spaCy, NLTK) ● Hands-on experience with LLMs (GPT, Claude, Llama, Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi More ❯

innovation, creating reusable frameworks, prototypes, and thought leadership What we’re looking for: ● Strong expertise in Python and leading AI/ML/NLP libraries (e.g. Hugging Face Transformers, spaCy, NLTK) ● Hands-on experience with LLMs (GPT, Claude, Llama, Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi More ❯

innovation, creating reusable frameworks, prototypes, and thought leadership What we’re looking for: ● Strong expertise in Python and leading AI/ML/NLP libraries (e.g. Hugging Face Transformers, spaCy, NLTK) ● Hands-on experience with LLMs (GPT, Claude, Llama, Mistral) and GenAI/Agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI) ● Proven track record designing scalable AI architectures (RAG, Graph RAG, multi 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 ❯

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 ❯