1 to 25 of 28 Keras Jobs

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 ❯

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 ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure More ❯

classification algorithms. • Experience with deep learning frameworks such as PyTorch, TensorFlow, and Hugging Face Transformers. • Practical experience in Natural Language Processing methods and libraries such as spaCy, word2vec, TensorFlow, Keras, PyTorch, Flair, BERT. • Practical experience with large language models, prompt engineering, fine-tuning, and benchmarking using frameworks such as LangChain and LlamaIndex. • Strong Python background. • Knowledge of AWS, GCP, Azure 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 ❯

foundation in statistical analysis and proven experience applying a range of machine learning techniques to solve business problems (e.g., regression, classification, clustering, time-series forecasting). Practical experience with Keras or PyTorch is required. Full-Stack Deployment: Demonstrable experience taking models to production, including building and deploying APIs with FastAPI and using Vertex AI for ML workflows. Visualization & Communication: Ability More ❯

of appropriate procedures for information governance, especially when using patient data Qualifications Essential Expertise building statistical models and use of machine learning using Python libraries such as Scikit-learn, Keras, and TensorFlow frameworks Experience in natural language processing Experience with one or more data management tools (SQL etc.) Demonstrable use of programming in collaborative and reproducible analysis pipelines Experience using More ❯

skills, a quick learning ability, and enthusiasm for tackling complex challenges. You are proficient in Python, with experience using PySpark and ML libraries such as scikit-learn, TensorFlow, or Keras . You are familiar with big data technologies (e.g., Hadoop, Spark), cloud platforms (AWS, GCP), and can effectively communicate technical concepts to non-technical stakeholders. Accommodation requests If you need 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 ❯

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 ❯

enabling efficient data transformation for downstream machine learning modeling and analysis. 3. Utilize knowledge of advanced data science techniques and tools, including Python, Apache Spark, SparkML, scikit-learn, TensorFlow, Keras, XGBoost, and Tidyverse, to build various ML-driven applications such as recommendation models, timeseries forecasting, attribution models that address business needs and deliver impactful solutions. 4. Utilize knowledge of AWS … large-scale data processing, enabling seamless preparation for downstream modeling and analysis. 10. Utilize Python, machine learning, supervised and unsupervised algorithms, and statistical modeling with SparkML, scikit-learn, TensorFlow, Keras, XGBoost, and Tidyverse to build large-scale machine learning models for clustering, segmentation, cancer detection, etc. 11. Utilize AWS SageMaker and Google Cloud Platform for scalable batch inferencing and modeling 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 ❯