14 of 14 Keras Jobs in the UK excluding London

Engineer to join our growing teams in Erskine or Newcastle. Key Responsibilities Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using More ❯

experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine learning and large language models (LLMs), encompassing deployment, monitoring, and retraining. Familiarity with software engineering guidelines: version control (e.g., Git), CI/CD, containerization (e.g. More ❯

and PyTorch. Programming: Solid experience in Python and SQL. Experience with R is a nice-to-have. ML and AI: Practical experience using ML modeling libraries like Scikit-Learn, Keras, Tensorflow, PyTorch and similar Generative AI: Some hands-on experience with LLMs for prompt engineering or agents is preferred Cloud Expertise: Building, deploying and monitoring models on cloud like Azure More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

Pandas, NumPy and scikit-learn. Statistical & Machine Learning Modelling : Experience with a variety of ML techniques ( regression, classification, clustering, time-series forecasting ) Experience with deep learning frameworks such as Keras or PyTorch Model Deployment : Proven ability to productionise models, including building and deploying APIs Strong visualization and communication skills , with the ability to translate complex technical findings into actionable insights More ❯

s3) Experience with software development life cycle (sdlc) and agile/iterative methodologies Experience in using Python and hands on experience building models with deep learning frameworks like Tensorflow, Keras, PyTorch, MXNet Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and More ❯

Vertex AI, Azure AI Services). Experience with a major conversational AI platform (Google Dialogflow, Amazon Lex, Rasa, or similar). A solid understanding of core Python ML libraries (Keras, scikit-learn, Pandas) and deep learning frameworks (TensorFlow, PyTorch). Desirable (but not essential) experience: Working with tools/interfaces for AI applications e.g. MCP protocol. Training traditional ML and More ❯

with AI systems, machine learning models, or related technologies using Microsoft AI tools and Azure. Technical Skills Needed Familiarity with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Keras). Experience in Microsoft Azure as well as Microsoft AI tools (Copilot) is a must. Proficient in programming languages such as Python, Java, or C++. Understanding of system architecture and More ❯

to make an immediate contribution and strengthen their profile in AI for neurotechnology through impactful short-term work. - AI/ML research, particularly deep learning using platforms such as Keras, PyTorch, or TensorFlow - Developing and testing novel or customised AI models for complex, noisy, real-world datasets - Software optimisation for high-performance computing on CPU and GPU clusters - VR/ More ❯

to make an immediate contribution and strengthen their profile in AI for neurotechnology through impactful short-term work. - AI/ML research, particularly deep learning using platforms such as Keras, PyTorch, or TensorFlow - Developing and testing novel or customised AI models for complex, noisy, real-world datasets - Software optimisation for high-performance computing on CPU and GPU clusters - VR/ More ❯

to make an immediate contribution and strengthen their profile in AI for neurotechnology through impactful short-term work. - AI/ML research, particularly deep learning using platforms such as Keras, PyTorch, or TensorFlow - Developing and testing novel or customised AI models for complex, noisy, real-world datasets - Software optimisation for high-performance computing on CPU and GPU clusters - VR/ More ❯

to make an immediate contribution and strengthen their profile in AI for neurotechnology through impactful short-term work. - AI/ML research, particularly deep learning using platforms such as Keras, PyTorch, or TensorFlow - Developing and testing novel or customised AI models for complex, noisy, real-world datasets - Software optimisation for high-performance computing on CPU and GPU clusters - VR/ More ❯

team members in identifying and addressing data science opportunities. Required Skills & Experience Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using … problem-solving skills and ability to work independently or in a team. Strong proficiency in Python and ML libraries such as: pandas, NumPy, scikit-learn XGBoost, LightGBM, CatBoost TensorFlow, Keras, PyTorch Experience with model deployment and serving tools: ONNX, TensorRT, TensorFlow Serving, TorchServe Familiarity with ML lifecycle tools: MLflow, Kubeflow, Azure ML Pipelines Experience working with distributed data processing using More ❯