1 to 25 of 78 Keras Jobs in London

br - Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform br - Working knowledge of one or more ML engineering frameworks (e.g. TensorFlow, PyTorch, Keras, Scikit-Learn) br - Working knowledge of object-oriented programming and unit testing in Python br - Working knowledge of application and information security principles and practices (e.g. OWASP for Machine Learning 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

modelling techniques and how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, ResNets, VAEs, GANs, Markov chains, etc. Experience using specialized machine learning libraries eg. 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 ❯

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 ❯

projects involving models like GANs, VAEs, transformers, or diffusion models Proficiency in programming languages such as Python, R, or Julia, and experience with ML frameworks like TensorFlow, PyTorch, or Keras Strong understanding of deep learning architectures, neural networks, and statistical modeling techniques Experience with cloud platforms (AWS, Azure, Google Cloud) and MLops tools for model deployment and management Excellent problem More ❯

analysis, Counterfactuals Estimation, Optimization and Scenarios simulation Good experience with Probabilistic Programming and Bayesian Methods Experience using machine learning frameworks such as scikit-learn, Pymc, TensorFlow, PyTorch, Databricks ML, Keras etc..., Experience with ML at scale Proven attention to detail, critical thinking, and the ability to work both independently and collaboratively within a cross-functional team Good communication skills including More ❯

Spark. Preferred Will be curious, enjoy problem solving and have empathy for the problems you are challenged with Working knowledge on data science frameworks and toolkits (Scipy, Scikit-Learn, Keras, PyTorch, Etc.) Experience with Git, Databricks and Microsoft Azure technical stack Prior exposure to Agile methodologies Experience building large scale machine learning systems Master's Degree in Computer Science, Data More ❯

machine learning, and business analytics Hands-on experience with ML techniques such as XGBoost, deep neural networks, and transformers Practical knowledge of ML libraries and frameworks such as Fastai, Keras, TensorFlow, PyTorch, and Scikit-learn Ability to research, understand, and apply emerging machine learning techniques Programming & Data Engineering Proficiency in programming languages such as Python (preferred) and C++ Experience working More ❯

audio, video) • Familiar with Machine Learning (ML) models like XGBoost, deep neural networks, transformers, Markov chains • Experienced in using Machine Learning (ML) libraries such as scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face Cloud & Deployment • Hands-on experience with AWS, Azure, or GCP • Understanding of API integration and cloud-based Machine Learning (ML)/AI deployment Research & Innovation • Ability to read More ❯

Machine Learning Engineer - SaaS - London (Tech stack: Machine Learning Engineer, Python, TensorFlow, PyTorch, scikit-learn, Keras, Natural Language Processing (NLP), Hugging Face Transformers, Pandas, NumPy, Jupyter Notebooks, Matplotlib, Seaborn, Flask (for building APIs), FastAPI, Docker, MLflow, DVC (Data Version Control), AWS SageMaker, Azure Machine Learning, Google Cloud AI Platform, TensorFlow Serving, ONNX (Open Neural Network Exchange) We have several exciting … experience in some or all of the following (full training will be provided to fill any gaps in your skill set): Machine Learning Engineer, Python, TensorFlow, PyTorch, scikit-learn, Keras, Natural Language Processing (NLP), Hugging Face Transformers, Pandas, NumPy, Jupyter Notebooks, Matplotlib, Seaborn, Flask (for building APIs), FastAPI, Docker, MLflow, DVC (Data Version Control), AWS SageMaker, Azure Machine Learning, Google More ❯

Machine Learning Engineer - SaaS - London (Tech stack: Machine Learning Engineer, Python, TensorFlow, PyTorch, scikit-learn, Keras, Natural Language Processing (NLP), Hugging Face Transformers, Pandas, NumPy, Jupyter Notebooks, Matplotlib, Seaborn, Flask (for building APIs), FastAPI, Docker, MLflow, DVC (Data Version Control), AWS SageMaker, Azure Machine Learning, Google Cloud AI Platform, TensorFlow Serving, ONNX (Open Neural Network Exchange) We have several exciting … experience in some or all of the following (full training will be provided to fill any gaps in your skill set): Machine Learning Engineer, Python, TensorFlow, PyTorch, scikit-learn, Keras, Natural Language Processing (NLP), Hugging Face Transformers, Pandas, NumPy, Jupyter Notebooks, Matplotlib, Seaborn, Flask (for building APIs), FastAPI, Docker, MLflow, DVC (Data Version Control), AWS SageMaker, Azure Machine Learning, Google More ❯