embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
embeddings, and sequence classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of More ❯
stack and reporting tools. Core Responsibilities Develop, fine-tune, and deploy transformer-based NLP models (LLMs, BERT, RoBERTa, GPT-family). Design and implement scalable data pipelines using Python, spaCy, Pandas, and Hugging Face Transformers. Build or enhance retrieval-augmented generation (RAG) systems using LangChain and vector databases like FAISS, Weaviate, or Pinecone. Package and deploy solutions via Docker, Kubernetes More ❯
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 ❯
and training workflows using PyTorch or TensorFlow. Develop and maintain end-to-end ML pipelines, from data preprocessing to model deployment. Leverage the Python ecosystem (NumPy, pandas, scikit-learn, spaCy, NLTK, Hugging Face Transformers) for feature engineering, model development, and evaluation. Manage and track machine learning experiments using MLflow, ensuring reproducibility, versioning, and lifecycle management. Collaborate with data scientists, software More ❯
by local law. What you bring Basic 5+ years of experience in designing, developing, deploying and monitoring machine learning and deep learning solutions Experience programming in Python, PyTorch, TensorFlow, spaCy, scikit learn, or equivalent machine learning framework Strong programming experience in one or more of the following languages - Python, C#, Java, C/C++ Bachelor's degree in computer science 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 ❯
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 ❯
