1 to 25 of 59 Permanent scikit-learn Jobs in the South East

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

closely related field Strong foundations in machine learning, algorithms, and statistics Experience with Python and common ML libraries (e.g. TensorFlow, PyTorch, scikit-learn) Understanding of software engineering fundamentals and version control (e.g. Git) Ability to communicate technical ideas clearly to both technical and non-technical stakeholders Right ...

Engineer will have the following skills and experience: Strong Python and SQL experience, ideally with Databricks Experience with ML and LLM tools (e.g., scikit learn, XGBoost, PyTorch, TensorFlow, RAG, APIs) Comfortable with data engineering basics and MLOps practices Experience working with cloud platforms such as Azure ...

enterprise environments Deep understanding of algorithm selection, model complexity trade-offs, and overfitting/underfitting dynamics Strong proficiency in Python and ML ecosystems (scikit-learn, pandas, NumPy) Experience with deep learning frameworks (PyTorch or TensorFlow) Practical knowledge of deep learning architectures (CNNs, RNNs, Transformers) and when to apply ...

will not be considered) Experience with CI/CD pipelines for ML/DL models Proficiency in Python and common ML libraries (e.g. Scikit-learn, TensorFlow, PyTorch) Experience with container technologies (Docker, Kubernetes) Consultancy or client-facing experience preferred Strong communication and stakeholder management skills Qualifications IT/ ...

engineers to forecast key company KPIs, design real-time dashboards, and develop best-in-class data infrastructure. Key Requirements Strong Python (pandas, NumPy, scikit-learn; PyTorch/TensorFlow a plus) and SQL skills Experience applying advanced analytics or ML to real-world data (finance, fintech, or forecasting contexts ...

learn) Docker-based environments • Clear written communication skills Nice to have: • Experience with model training, fine-tuning, or ML frameworks (Python, PyTorch, scikit-learn) • Building and deploying ML data pipelines • AWS experience (ECS, S3, RDS, Elastic Beanstalk) • Redis and queue-based architectures • Third-party API integrations (Stripe ...

memory management. Experience with widely used tools in data science and machine learning. Developers should be somewhat familiar with libraries like NumPy, pandas, scikit-learn, and PyTorch. Proficiency in testing approaches and frameworks like pytest as well as automation tools like Github Actions or Travis CI for maintaining ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

engineers, and DevOps, with the freedom to shape how GenAI and ML are used across the platform. Tech stack: Python PyTorch or TensorFlow scikit-learn Spark Kafka Databricks LangChain and RAG-based GenAI systems Cloud-native Azure environment Important Details: Fully remote across ...

Machine Learning Strong experience in Computer Vision (e.g. object detection, image classification, segmentation) Experience building, training and optimising ML models Python (NumPy, Pandas, scikit-learn) and frameworks such as TensorFlow or PyTorch Experience deploying models into production environments (cloud platforms, Docker, CI/CD pipelines) Clearance requirements: Active ...

detection logic, and communicate findings effectively to non-technical stakeholders. Essential Skills & Experience Strong Python for data processing and analytics (e.g., pandas, numpy; scikit-learn or equivalent) Structured data expertise: joins, aggregations, data cleaning, handling missing data/outliers, basic data modelling concepts Feature engineering: ability to craft ...