20 of 20 Permanent Keras Jobs in the UK

of machine learning algorithms including supervised, unsupervised, and reinforcement learning approaches Strong proficiency in statistical modeling, time-series forecasting, and predictive analytics Experience with deep learning frameworks (TensorFlow, PyTorch, Keras) Knowledge of prompt engineering, RAG (Retrieval Augmented Generation), and LLM fine-tuning techniques Understanding of natural language processing, computer vision, and recommender systems Essential: Hands-on experience with at least More ❯

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 ❯

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 ❯

/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools such as Terraform . Strong background in technology deployment More ❯

/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools such as Terraform . Strong background in technology deployment More ❯

/RFP responses, bid documentation, and client presentations. Hands-on experience with data science platforms such as Databricks , Dataiku , AzureML , or SageMaker , and machine learning frameworks such as TensorFlow , Keras , PyTorch , and scikit-learn . Expertise in cloud platforms ( AWS , Azure , Google Cloud ) and experience deploying solutions using cloud provisioning tools such as Terraform . Strong background in technology deployment 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 ❯

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 ❯

looking for: Prior experience in a Data Science/Machine Learning/Engineering role Strong Python skills and experience writing production-grade code Familiarity with ML frameworks (TensorFlow, PyTorch, Keras, SKLearn) Proficiency with Git, Unix/Linux, Docker Cloud experience (AWS, Azure, or similar) Excellent communication skills UK citizen eligible for SC clearance Nice to have: MLOps experience Open source More ❯

3+ years of experience in a Data Science/Machine Learning/Engineering role Strong Python skills and experience writing production-grade code Familiarity with ML frameworks (TensorFlow, PyTorch, Keras, SKLearn) Proficiency with Git, Unix/Linux, Docker Cloud experience (AWS, Azure, or similar) Excellent communication skills UK citizen eligible for SC clearance Nice to have: MLOps experience Open source More ❯

into production environments. Strong Python skills and experience with LLM frameworks (LangChain, LlamaIndex, HuggingFace Transformers). Knowledge of conversational AI platforms (Dialogflow, Lex, Rasa). Familiarity with TensorFlow , PyTorch , Keras , scikit-learn , and Pandas . Experience integrating with cloud-based AI services (AWS, Google Cloud, Azure). The ability to communicate complex technical concepts clearly and confidently. Desirable (Not Essential 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 ❯

Skills Proven experience designing and deploying Gen AI systems using LLMs, transformers, and neural networks Expert-level Python; strong in R, Java, or C++ Hands-on with TensorFlow, PyTorch, Keras, Hugging Face, LangChain Cloud-native mindset: AWS, Azure, GCP + Docker, Kubernetes, CI/CD Deep understanding of ML/DL algorithms, model evaluation, and data engineering Strong communicator and More ❯

in a high-growth environment, while maintaining high standards of quality and attention to detail. Bonus: Existing interest in standard machine learning and deep-learning packages (e.g., scikit-learn, Keras, PyTorch). Exposure to commercial engineering tools or optimisation packages (e.g., Dassault Systèmes, Siemens, ANSYS, Aspen), open-source optimisation tools used in industry and academia. What we do We are 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 ❯

Frontend (e.g. React) and backend (e.g. Node.js) development Machine Learning & Artificial Intelligence Machine Learning (General concepts, model development, experimentation, training, evaluation) Deep Learning (General, frameworks like TensorFlow, PyTorch, JAX, Keras, neural networks, CNNs, RNNs, transformer architecture, LSTM) Natural Language Processing (NLP) Reinforcement Learning (e.g., PPO, Q-learning, policy gradients, A2C, DQN, AlphaZero) Computer Vision (e.g., image processing, analysis, instance segmentation 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 ❯

LLM features in a regulated environment with sensitive data. You’ll work with engineering to move models from idea to production. Tech Stack: Python, scikit-learn, PyTorch, TensorFlow/Keras, LangChain, Pydantic, Spark, Kafka, Databricks, MLflow, Azure, Docker, Kubernetes. Salary: Up to £100k Working Environment: Fully Remote in UK What you’ll do Research, prototype, and deploy LLM use cases … Q&A, summarisation, document processing) Build and tune models using scikit-learn, PyTorch, TensorFlow/Keras, and XGBoost Create data pipelines for batch and streaming with Spark and Kafka Use Databricks and MLflow for experiments, deployment, and monitoring What you’ll bring Hands-on Python for data science and ML Practical experience with LLM frameworks (e.g. LangChain) Familiar with Spark More ❯