1 to 25 of 130 Permanent Feature Engineering Jobs in the UK excluding London

working across the full ML lifecycle—from design through to production—in a modern, AWS-based environment. You'll be part of an engineering and data team tackling projects like LLM development, intelligent automation, semantic search, and recommendation systems. There's real scope here to influence how MLOps … doing: Building and deploying ML models and LLM applications Fine-tuning large language models for specific use cases Working with prompt engineering and RAG systems Creating end-to-end ML pipelines—ingestion, feature engineering, deployment Developing LLM-powered tools: chatbots, automation, content generation Implementing MLOps practices: versioning ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...

design, build, deploy, and optimise machine learning models that power data-driven products and business solutions. This role bridges data science and software engineering, focusing on production-ready ML systems, scalability, and performance. The ideal candidate has strong experience in Python, ML frameworks, data pipelines, and cloud platforms … train, and evaluate machine learning models for prediction, classification, recommendation, or automation use cases. Apply supervised, unsupervised, and deep learning techniques as appropriate. Perform feature engineering, model tuning, and validation to improve accuracy and performance. 2. Productionisation & Deployment Deploy ML models into production using scalable, reliable architectures. Build ...