12 of 12 scikit-learn Jobs in the North of England

customers. Machine Learning, predictive modelling and applied AI, developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large ...

customers. Machine Learning, predictive modelling and applied AI, developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large ...

customers. Machine Learning, predictive modelling and applied AI , developing, evaluating and operationalising models using state-of-the-art libraries and frameworks such as scikit-learn, TensorFlow, PyTorch and XGBoost, helping tackle complex challenges including in areas like fraud prevention, credit risk management and customer personalisation. Generative AI, Large ...

following areas would be highly beneficial: - Machine Learning and AI Engineering- Generative AI and Large Language Models- Prompt engineering and context engineering- scikit-learn, PyTorch or TensorFlow- LangChain or similar AI frameworks- Cloud data platforms and services across AWS, Azure or GCP- Data warehouses and modern data platforms ...

client projects Requirements 5+ years of experience in machine learning engineering or applied ML research Strong proficiency in Python, PyTorch or TensorFlow, and Scikit-learn Experience deploying ML models at scale using Docker, Kubernetes, or serverless Deep understanding of ML fundamentals: optimisation, regularisation, evaluation metrics Experience with cloud ...

Experience with Cloud AI services such as Azure ML, AWS SageMaker, GCP Vertex AI. Hands-on experience with machine learning libraries such as scikit-learn, TensorFlow, or PyTorch. Practical knowledge of algorithms and techniques such as regression, decision trees, random forest, XGBoost, k-means, anomaly detection, and time ...

Desirable Experience Machine Learning model development and training. LLMs, transformers, RAG and Agentic AI. LangChain, LangGraph or similar AI orchestration frameworks. PyTorch, TensorFlow, Scikit-learn or Hugging Face. Vector databases, embeddings and model evaluation. NLP, Computer Vision or multimodal AI. Edge AI, AI safety, security or explainability. Experience ...

experiment with new approaches and still care about getting models into production properly. What you could be working across Python PyTorch, TensorFlow or scikit-learn LLMs, RAG, agents and evaluation ML research and rapid prototyping MLOps and production ML pipelines Model deployment, monitoring and lifecycle tooling AWS, Docker ...

concepts, automated testing, continuous integration and continuous delivery, and production deployments. Commercial experience using key Python libraries for machine learning, such as Pandas, Scikit-learn and XGBoost. Proven development experience with cloud services, data stores and architectures, such as Vertex AI, BigQuery and Cloud Run. Hands-on experience ...

such as statistical modelling, supervised and unsupervised learning, deep learning, transformers, Generative AI or Agentic AI. Experience across areas such as PyTorch, TensorFlow, scikit-learn, Hugging Face, cloud platforms, MLOps, computer vision, NLP, geospatial data or multimodal AI would also be of interest, although candidates are not expected ...

Proven success in leading the design, development, and deployment of sophisticated predictive models. Expertise in Python/R and ML frameworks, such as scikit-learn, TensorFlow, or PyTorch, Proven ability to design and implement complex machine learning solutions and guiding best practice. Deep understanding of statistical analysis ...

Raffles, Singapore | Posted on 04/06/2026 Experience working with ML platforms such as CML, SparkMLlib, and Python ML libraries (scikit‐learn, XGBoost), including modeldeployment. Design and develop highly scalable, Real time systemsusing Hadoop ecosystem components(Iceberg, Spark, Ozone, Trino, Hive,Ranger, Kafka, Flink and Nifi ...