1 to 25 of 109 scikit-learn Jobs in the UK

experience in a machine learning, AI engineering, or applied data science role Strong Python skills, with experience in ML frameworks (e.g. PyTorch, TensorFlow, scikit-learn) Experience deploying models to production (cloud platforms such as Azure, AWS, or GCP) Understanding of MLOps practices - CI/CD for ML, model ...

following: Commercial experience developing AI or Machine Learning solutions Strong Python development skills Experience with Machine Learning frameworks such as TensorFlow, PyTorch or Scikit-learn Experience with Generative AI technologies including LLMs, LangChain or LlamaIndex Hands-on experience building RAG solutions and working with vector databases Experience with ...

detection. Provide mentorship and technical guidance to junior AI Scientists. Skills: Strong proficiency in Python and experience with frameworks such as NumPy, Pandas, Scikit-learn, and modern Generative AI libraries (e.g., LangChain, LlamaIndex, Azure AI Foundry). Hands‐on experience with PyTorch, leading LLM libraries such as Hugging ...

Experience Experience building and deploying AI or Machine Learning solutions in a commercial environment Strong Python and SQL skills Experience using TensorFlow, PyTorch, Scikit-learn or similar frameworks Strong understanding of machine learning algorithms, deep learning architectures and model deployment practices Experience with Azure, AWS or GCP cloud ...

detection.* Provide mentorship and technical guidance to junior AI Scientists**Skills*** Strong proficiency in Python and experience with frameworks such as NumPy, Pandas, Scikit-learn, and modern Generative AI libraries (e.g., LangChain, LlamaIndex, Azure AI Foundry).* Hands-on experience with PyTorch, and leading LLM libraries such ...

Machine Learning Engineer Strong experience developing and deploying Machine Learning models using Python Experience with frameworks such as PyTorch, TensorFlow, Scikit-learn or XGBoost Knowledge of AWS cloud services including SageMaker, Lambda and S3 Experience with MLOps tools such as MLflow, Weights & Biases or Data Version Control Experience ...

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 ...

writing clean, efficient code for AI/ML solutions. Hands-on experience, through study, internships, or personal projects, with machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch). Exposure to supporting the deployment of AI/ML models, working alongside engineering colleagues. Some exposure to generative AI concepts ...

tooling (e.g. MLflow, Weights & Biases, DVC, Airflow) Strong experience with Python and associated scientific software packages such as PyTorch, OpenCV, Pandas, SciPy, NumPy, SciKit-learn, etc. Strong software engineering practices including version control, code review, software testing methodologies, and continuous integration; experience with IEC 62304 is particularly desirable ...

leading ML/AI technical initiatives across multiple teams Strong experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management ...

lifecycle expertise: feature engineering, model training, evaluation and production deployment, including MLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit‐learn, PyTorch, XGBoost) and cloud‐native ML services (SageMaker, Azure ML, Vertex AI). Architecture and delivery of scalable cloud data and AI platforms ...

degree in machine learning, computer science, engineering, or a related discipline, with an MScrequiredand a PhD highly desirable. Deep hands-on experience withPyTorch, Scikit-learn, and the Hugging Face ecosystem, with familiarity withMLFlowandAzureML; capable of making architecture and implementation decisions. Strong competence with Git, pull requests, automated testing ...

Proficiency in SQL including strong knowledge of database structure and efficiency Proficiency in Git Proficiency in AI/ML frameworks: Huggingface, Torch, TensorFlow, Scikit‐Learn, and Spacy Experience in MLOps platforms: MLFlow, dbt, Docker, Kubernetes Experience with cloud environments such as AWS Technical Concept Requirements Experience ...

lifecycleexpertise: feature engineering, model training,evaluationand production deployment, includingMLOps, monitoring and drift detection. Practical knowledge of ML frameworks (e.g. scikit-learn,PyTorch,XGBoost) and cloud-native ML services (SageMaker, Azure ML, Vertex AI). Platform & Cloud Engineering Architecture and delivery of scalable cloud data and AI platforms ...

production and supporting them post-deployment.* Strong Python programming skills, including object-oriented design and proficiency with key ML libraries (e.g., PyTorch, TensorFlow, Scikit-Learn).* Solid understanding of probability and statistical modeling to support robust model development and interpretation.* Experience with cloud platforms (especially Azure and/ ...

Data Engineering, or Software Engineering roles Strong production Python experience Hands‐on experience training and serving ML models Familiarity with PyTorch, TensorFlow, or scikit‐learn Strong working knowledge of Docker, Linux, Git, and cloud platforms (AWS, Azure, or similar) Experience deploying systems you’ve helped design Excellent communication ...

Python and SQL, with experience in data manipulation, model development, and analytical workflows. Hands‐on experience with machine learning frameworks such as scikit‐learn, TensorFlow, PyTorch, or similar. Strong experience with cloud platforms (AWS, Azure, or GCP) and building cloud‐native systems. Experience with modern data platforms such ...

science practices tooling governance Essential Skills for the Data Scientist: Strong experience in data science using Python with libraries such as Pandas NumPy scikit-learn or similar Solid grounding in statistics probability machine learning techniques Proven experience working within cloud environments such as AWS Azure or GCP Experience ...

Data Science, Mathematics, Computer Science, or a related field. Strong programming skills in Python or R, with experience using frameworks such as Scikit-learn, PyTorch, or TensorFlow. Experience with cloud platforms or distributed computing (GCP preferred, but AWS or Azure also welcome). Exposure to neural networks ...

Data Science, Mathematics, Computer Science, or a related field. Strong programming skills in Python or R, with experience using frameworks such as Scikit-learn, PyTorch, or TensorFlow. Experience with cloud platforms or distributed computing (GCP preferred, but AWS or Azure also welcome). Exposure to neural networks ...

computer vision projects Knowledge of AWS, Azure, or GCP Familiarity with Docker, APIs, or Git workflowsFamiliarity with frameworks such as PyTorch, TensorFlow, or Scikit-learn Internship, university project, or personal portfolio related to AI/ML What’s on Offer Competitive graduate salary package Company stock options/ ...

track record delivering impactful ML/AI solutions in production. Deep expertise in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, NumPy, Pandas). Hands-on experience with GenAI, agentic AI, and automated testing for AI systems. Curiosity and creativity to challenge assumptions ...

including proficiency with libraries like Pandas and NumPy. Machine Learning & Frameworks: Deep practical knowledge of ML techniques and popular frameworks (eg, TensorFlow, PyTorch, scikit-learn). Advanced Data Analytics: Solid grasp of graphs, time-series modelling, numerical algorithms, and geospatial analytics. Leadership & Outcomes: Proven experience leading technical teams ...

including proficiency with libraries like Pandas and NumPy. Machine Learning & Frameworks: Deep practical knowledge of ML techniques and popular frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Advanced Data Analytics: Solid grasp of graphs, time-series modeling, numerical algorithms, and geospatial analytics. Leadership & Outcomes: Proven experience leading technical teams ...

data and implementing data science processes. Experience in translating customer requirements intotechnical requirements and development objectives. Pythonexpertisecovering standard data sciencelibraries (NumPy, pandas, matplotlib, Scikit-learn,PyTorch, etc),web frameworks (Flask or FastAPI) and visualisation(Dash,Plotly,&Streamlit). Proficient inSQL. Working with unstructured data Building customer and stakeholder ...