1 to 25 of 126 Keras Jobs in England

SageMaker or Azure ML), and optimising models for efficient inference using formats like ONNX and TensorRT Proficiency in Python and ML/engineering frameworks such as PyTorch, TensorFlow (including Keras), Hugging Face (Transformers, Datasets) and scikit-learn, etc Experience with MLOps tools, including MLFlow, workflow orchestrators (Airflow, Metaflow, Perfect or similar), and containerisation (Docker) Strong knowledge of cloud platforms like More ❯

TensorFlow, AI Foundry, OpenAI, Scikit-learn etc.). • Solid understanding of core machine learning concepts • Hands-on experience with at least one major deep learning framework (e.g., TensorFlow, PyTorch, Keras). • Proven understanding of software engineering principles, data structures, algorithms, and design patterns. • Experience with version control systems (e.g., Git). • Excellent problem-solving and analytical skills. • Strong communication and More ❯

Experience with Python, ML libraries (e.g. spaCy, NumPy, SciPy, Transformers, etc.), data tools and technologies (Spark, Hadoop, Hive, Redshift, SQL), and toolkits for ML and deep learning (SparkML, Tensorflow, Keras). Demonstrated ability to work on multi-disciplinary teams with diverse skillsets. Deploying machine learning models and systems to production (DevOps, MLOps, CI/CD). Experience of working in More ❯

GenAI prompting and augmentation for textual analysis, with an interest in learning more. Experience working with commonly used data science libraries and frameworks, e.g. Spacy, pandas, numpy, scikit-learn, Keras/TensorFlow, PyTorch, LangChain, Huggingface transformers etc. Familiar with both on-premises and cloud-based platforms (e.g. AWS). Working understanding of ML Ops workflows and ability to perform basic More ❯

and public sector clients, including participating in RFI/RFP processes, preparing bids, and delivering presentations. Familiarity with data science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., Keras, TensorFlow, PyTorch, scikit-learn). Experience deploying solutions on Cloud platforms (AWS, Azure, Google Cloud) using provisioning tools like Terraform. Proven ability to deploy technologies such as Docker, Kubernetes, CI More ❯

RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment– proven More ❯

RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment– proven More ❯

RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms – demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment– proven More ❯

RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment- proven More ❯

RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment- proven More ❯

RFI/RFP process, as preferred bidder, documented bids and face to face presentations. Experience of data science platforms (e.g. Databricks, Dataiku, AzureML, SageMaker) and machine learning frameworks (e.g. Keras, Tensorflow, PyTorch, scikit-learn) Cloud platforms - demonstrable experience of building and deploying solutions to Cloud (e.g. AWS, Azure, Google Cloud) including Cloud provisioning tools (e.g. Terraform). Technology deployment- proven More ❯

Data Pipelining Tools: Apache NiFi, Apache Kafka, and Apache Flink. ETL Tools: AWS Glue, Azure Data Factory, Talend, and Apache Airflow. AI & Machine Learning: Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras, and MXNet. AI Services: AWS SageMaker, Azure Machine Learning, Google AI Platform. DevOps & Infrastructure as Code: Containerization: Docker and Kubernetes. Infrastructure Automation: Terraform, Ansible, and AWS CloudFormation. API & Microservices: API More ❯

language models (LLMs) and prompt engineering for business applications. Strong programming skills ( Python, R, Java ) for AI model development and deployment. Familiarity with AI libraries and frameworks (TensorFlow, PyTorch, Keras). Experience deploying AI models in cloud computing environments (AWS, Azure, Google Cloud). Deep understanding of enterprise AI architecture, data engineering, and model optimization . Strong problem-solving skills More ❯

language models (LLMs) and prompt engineering for business applications. Strong programming skills ( Python, R, Java ) for AI model development and deployment. Familiarity with AI libraries and frameworks (TensorFlow, PyTorch, Keras). Experience deploying AI models in cloud computing environments (AWS, Azure, Google Cloud). Deep understanding of enterprise AI architecture, data engineering, and model optimization . Strong problem-solving skills More ❯

role, with hands-on experience in data science engineering. Proficiency in programming languages such as Python and SQL. Experience with data processing and analysis tools and technologies like TensorFlow, Keras, Scikit-learn. Strong understanding of large language models (LLMs) and machine learning (ML) algorithms and techniques. Familiarity with data visualization tools such as Power BI. Solid understanding of statistical analysis More ❯

containerization like Docker/Kubernetes). we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one of our Recruitment Partners. This More ❯

containerization like Docker/Kubernetes). we'd also encourage you to apply if you possess: Experience with Spark/Databricks. Experience deploying ML models via APIs (e.g., Flask, Keras). Startup experience or familiarity with geospatial and financial data. The Interview Process (subject to change): You’ll start with an introductory call with one of our Recruitment Partners. This More ❯

of machine learning modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using More ❯

Direct message the job poster from Dabster At Dabster, we specialize in connecting top talent with leading global companies. We are currently seeking an experienced Gen AI Architect to join our client's team in London, UK . Our mission More ❯

Location: Crawley, United Kingdom In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and More ❯

Location: Crawley, United Kingdom In fast changing markets, customers worldwide rely on Thales. Thales is a business where brilliant people from all over the world come together to share ideas and inspire each other. In aerospace, transportation, defence, security and More ❯

Requirements Experience: 3-5 years of hands-on experience in AI and machine learning. Technical Expertise: Strong proficiency in Python. Expertise in machine learning frameworks such as TensorFlow, PyTorch, Keras, or similar. Machine Learning: Solid understanding of supervised and unsupervised learning, deep learning (CNNs, RNNs, GANs), and reinforcement learning. Data Handling: Proficient in data manipulation and analysis using tools like More ❯

role, with hands-on experience in data science engineering. Proficiency in programming languages such as Python and SQL. Experience with data processing and analysis tools and technologies like TensorFlow, Keras, Scikit-learn. Strong understanding of large language models (LLMs) and machine learning (ML) algorithms and techniques. Familiarity with data visualization tools such as Power BI. Solid understanding of statistical analysis More ❯

Computer Science, AI, Data Science, or related fields . Expertise in large language models (LLMs) and prompt engineering for business applications. Familiarity with AI libraries and frameworks (TensorFlow, PyTorch, Keras). Experience deploying AI models in cloud computing environments (AWS, Azure, Google Cloud). Deep understanding of enterprise AI architecture, data engineering, and model optimization . Strong problem-solving skills More ❯

strong foundational software engineering knowledge, including data structures, algorithms, system design, and OOP. You have advanced knowledge of LLM architectures and ML/DL frameworks (e.g. TensorFlow, PyTorch, LangChain, Keras, scikit-learn). You're ready to design, deploy and maintain production-grade Machine Learning systems. You're willing to champion best practices in code quality, testing, observability and MLOps. More ❯