1 to 25 of 92 XGBoost Jobs in the UK

communication, presentations), Expertise in at least one of the following domains: Computer Vision, Large Language Models (LLMs), Time Series Analysis, Experience with TensorFlow, PyTorch, XGBoost, LightGBM, Proficiency with Git, Linux, Docker, MLflow, Airflow, AWS/GCP/Azure, Solid SQL and data visualization skills. Nice to have Proven experience leading ...

feature engineering, validation, model evaluation and optimisation. Hands-on experience with Python-based machine learning libraries, especially Scikit-Learn and at least one of XGBoost, LightGBM, TensorFlow or PyTorch. Experience developing models beyond exploratory analysis into reusable, documented and testable assets, ideally with exposure to pilot or production environments. Strong ...

development, with practical experience across model development, testing and deployment. Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps ...

development, with practical experience across model development, testing and deployment. Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps ...

development, with practical experience across model development, testing and deployment. Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps ...

development, with practical experience across model development, testing and deployment. Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps ...

development, with practical experience across model development, testing and deployment. Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps ...

development, with practical experience across model development, testing and deployment. Experience with machine learning frameworks and libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps ...

hands-on experience developing, training, and deploying machine learning models.Practical experience with leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, XGBoost), including model selection, tuning, and evaluation.Experience implementing end-to-end machine learning workflows, including data preprocessing, feature engineering, model development, validation, and deployment to production ...

hands-on experience developing, training, and deploying machine learning models.Practical experience with leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, XGBoost), including model selection, tuning, and evaluation.Experience implementing end-to-end machine learning workflows, including data preprocessing, feature engineering, model development, validation, and deployment to production ...

hands-on experience developing, training, and deploying machine learning models.Practical experience with leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, XGBoost), including model selection, tuning, and evaluation.Experience implementing end-to-end machine learning workflows, including data preprocessing, feature engineering, model development, validation, and deployment to production ...

hands-on experience developing, training, and deploying machine learning models.Practical experience with leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, XGBoost), including model selection, tuning, and evaluation.Experience implementing end-to-end machine learning workflows, including data preprocessing, feature engineering, model development, validation, and deployment to production ...

hands-on experience developing, training, and deploying machine learning models.Practical experience with leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, XGBoost), including model selection, tuning, and evaluation.Experience implementing end-to-end machine learning workflows, including data preprocessing, feature engineering, model development, validation, and deployment to production ...

hands-on experience developing, training, and deploying machine learning models.Practical experience with leading AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, XGBoost), including model selection, tuning, and evaluation.Experience implementing end-to-end machine learning workflows, including data preprocessing, feature engineering, model development, validation, and deployment to production ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

with modern deep learning frameworks (e.g.PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn,XGBoost). Experience with data storage for AI, vector databases, semanticsearch,and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search, and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

with modern deep learning frameworks (e.g.PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g., GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn,XGBoost). Experience with data storage for AI, vector databases, semanticsearch,and knowledge graphs. Contributions to open-source AI projects or research publications. Familiarity with ...

strategic AI solutions. Software/Data Engineering: Python proficiency, API design, SQL, data integration, and ML/AI libraries (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers). Agentic AI Systems: Hands-on experience with agentic AI setups including LLM orchestration, tool use/function calling, RAG, agents, prompt engineering ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search and knowledge graphs. Actively contributes to open-source AI projects, research publications and industry ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search and knowledge graphs. Actively contributes to open-source AI projects, research publications and industry ...

modern deep learning frameworks (e.g. PyTorch, TensorFlow), fine-tuning or distillation of LLMs (e.g. GPT, Llama, Claude, Gemini), machine learning libraries (e.g. scikit-learn, XGBoost). Experience with data storage for AI, vector databases, semantic search and knowledge graphs. Actively contributes to open-source AI projects, research publications and industry ...