end machine learning lifecycle, including data ingestion, preprocessing, model training, deployment, and production monitoring 3+ years of experience with modern machine learning tools and libraries (scikit-learn, PyTorch, TensorFlow, spaCy, etc) with strong proficiency in Python 3+ years of experience with any of the following fundamental AI technologies: vector search, embedding models, recommender systems, supervised, unsupervised machine learning, deep learning 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 ❯
london (city of london), south east england, united kingdom
Glite Tech
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 ❯
