1 to 25 of 959 TensorFlow Jobs in England

Job Description Tadaweb is a pioneering technology company with roots in Luxembourg and a growing global presence, with offices in the United Kingdom, France, and the United States. For over 13 years, we’ve been on a mission to make More ❯

Experience Databricks Machine Learning Associate or Machine Learning Professional Certification. Familiarity with traditional machine learning tools such as Python, SKLearn, XGBoost, SparkML, etc. Experience with deep learning frameworks like TensorFlow or PyTorch. Knowledge of ML model deployment options (e.g., Azure Functions, FastAPI, Kubernetes) for real-time and batch processing. Experience with CI/CD pipelines (e.g., DevOps pipelines, Git More ❯

and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (eg, neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference. 2. … production). Hands-on expertise building and deploying deep learning models (eg, CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS … NLP). Hands-on experience with large-scale language models (LLMs) and prompt engineering (eg, GPT, BERT, T5 family). Familiarity with on-device or edge-AI deployments (eg, TensorFlow Lite, ONNX, mobile/Embedded inference). Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. Open-source contributions or published papers More ❯

and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. • Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. • Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference. 2. … production). • Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. • Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). • Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). • Experience designing scalable ML infrastructure on cloud platforms (AWS … NLP). • Hands-on experience with large-scale language models (LLMs) and prompt engineering (e.g., GPT, BERT, T5 family). • Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). • Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. • Open-source contributions or published papers More ❯

and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference. Strategic … production). Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS … NLP). Hands-on experience with large-scale language models (LLMs) and prompt engineering (e.g., GPT, BERT, T5 family). Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. Open-source contributions or published papers More ❯

learning algorithms and their practical applications, particularly in fraud prevention and user personalization. Experience designing, developing, and implementing advanced machine learning models. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, and scikit-learn. Data Engineering Skills: Proficiency in developing and maintaining real-time data pipelines for processing large-scale data. Experience with ETL processes for data ingestion and More ❯

at all levels Desirable: Experience with MLOps, including integration of machine learning pipelines into production environments, Docker, and containerization/orchestration (e.g., Kubernetes) Experience in deep learning development using TensorFlow or PyTorch libraries Experience with Large Language Models (LLMs) and Generative AI applications Advanced SQL proficiency, with experience in MS SQL Server or PostgreSQL Familiarity with platforms like Databricks More ❯

and Tools Utilize Azure Machine Learning and AI tools to manage model lifecycles. Leverage cloud platforms like Azure, AWS, and GCP for scalable ML model deployment. Employ frameworks like TensorFlow, PyTorch, and scikit-learn for model development. Data Engineering and Preparation Oversee data ingestion, cleaning, transformation, and feature engineering processes to ensure high-quality datasets. Work with large datasets … learning-focused role. Technical Skills Expertise in designing and deploying ML algorithms, AutoML tools, and AI applications. Proficiency with programming languages such as Python and R, and ML libraries (TensorFlow, PyTorch, scikit-learn). Hands-on experience with cloud platforms (Azure ML) and big data ecosystems (e.g., Hadoop, Spark). Strong understanding of CI/CD pipelines, DevOps practices More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

see from you: Strong understanding of a wide range of ML algorithms. Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g. TensorFlow, PyTorch). Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Experience with LLM application design and More ❯

see from you: Strong understanding of a wide range of ML algorithms Understanding of MLOps practices for managing and monitoring models in production. Experience with deep learning frameworks (e.g. TensorFlow, PyTorch) Proficiency in programming languages such as Python, SQL and R. Knowledge of SQL and NoSQL databases for data storage and retrieval. Strong software engineering skills, including version control More ❯

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field. Strong programming skills in Python. Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Proficiency in data manipulation and analysis using tools such as Polars, Pandas, NumPy, or SQL. Solid understanding of algorithms, statistics, and data structures. Experience with cloud More ❯

Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field. Strong programming skills in Python. Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Proficiency in data manipulation and analysis using tools such as Polars, Pandas, NumPy, or SQL. Solid understanding of algorithms, statistics, and data structures. Experience with cloud More ❯

in large 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 More ❯

in large 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 More ❯

Networks and foundational concepts of NLP tasks such as Tokenization, Named Entity Recognition (NER), Sentiment Analysis, Part-of-Speech (POS) Analysis. Experience or coursework in Deep Learning frameworks like TensorFlow or PyTorch. Strong problem-solving skills and a curiosity for learning new technologies. Good verbal and written communication skills. Familiarity with cloud platforms (e.g., Azure (preferable), AWS, or Google More ❯

deployment hurdles. Ability to translate business questions into analytical frameworks and interpret results for non-technical stakeholders. Strong proficiency in Python, SQL, and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch). Experience with model operationalization using tools like Docker, Kubernetes, MLflow, or SageMaker. Marketing KPIs knowledge: CTR, conversion rate, MQL to SQL, ROI, CLV, CAC, retention. Experience working … Prophet, or custom implementations for time series A/B testing frameworks (e.g., DoWhy, causalml) Programming & Data Tools : Python: Strong foundation in Pandas, NumPy, matplotlib/seaborn, scikit-learn, TensorFlow, Pytorch etc. SQL: Advanced querying for large-scale datasets. Jupyter, Databricks, or notebooks-based workflows for experimentation. Data Access & Engineering Collaboration : Comfort working with cloud data warehouses (e.g., Snowflake More ❯

field. - 4+ years of experience in developing and deploying machine learning models, with a strong focus on generative AI techniques. - Proficiency in programming languages such as Python, PyTorch, or TensorFlow, and experience with deep learning frameworks. - Strong background in natural language processing, computer vision, or multimodal learning. - Ability to communicate technical concepts to both technical and non-technical audiences. More ❯

Bricks/Data QISQL for data access and processing (PostgreSQL preferred, but general SQL knowledge is important) Latest Data Science platforms (e.g., Databricks, Dataiku, AzureML, SageMaker) and frameworks (e.g., TensorFlow, MXNet, scikit-learn) Software engineering practices (coding standards, unit testing, version control, code review) Hadoop distributions (Cloudera, Hortonworks), NoSQL databases (Neo4j, Elastic), streaming technologies (Spark Streaming) Data manipulation and More ❯

or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience). Technical Skills: Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms. Experience in retraining and fine-tuning LLMs using large-scale datasets. Familiarity with cloud platforms More ❯

degree in 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 More ❯

or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience). Technical Skills: Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms. Experience in retraining and fine-tuning LLMs using large-scale datasets. Familiarity with cloud platforms More ❯

programming languages such as Python or R. Experience with SQL and familiarity with database management practices to handle and query large datasets efficiently. Knowledge of machine learning frameworks like TensorFlow or PyTorch and libraries such as scikit-learn. Proficiency in using data analysis and visualization tools to interpret data and present insights effectively. Proven experience as a Data Scientist More ❯

retraining mechanisms Conduct experiments using A/B testing and statistical analysis to validate approaches Document ML systems and provide support for ongoing performance tuning Use tools like Python, TensorFlow, PyTorch, Scikit-learn, AWS, GCP, MLflow, Docker, SQL , and others Requirements Minimum Bachelor's degree in Computer Science, Machine Learning, AI, or a related field Proven experience as a … Machine Learning Engineer or in a similar role (minimum 1 year ) Strong proficiency in Python and popular ML frameworks (e.g., TensorFlow, PyTorch) Experience deploying machine learning models into production environments Solid understanding of data structures , algorithms , and statistical learning Familiarity with cloud platforms (AWS, Azure, or GCP) and ML pipeline orchestration Bonus: Experience with deep learning , NLP , recommendation systems More ❯