76 to 100 of 510 TensorFlow Jobs in England

reviews, and technical documentation. What We're Looking For 3+ years of experience working on AI/ML systems in production environments. Proficiency in Python and ML frameworks like TensorFlow, PyTorch, scikit-learn. Experience designing and deploying models using cloud platforms (AWS, GCP, or Azure). Solid understanding of data structures, algorithms, and software engineering principles. Experience with APIs More ❯

not only technically strong, but solution-oriented, strategically minded, and able to communicate insights clearly to both technical and non-technical audiences. Requirements: Advance Python (Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch) & SQL skills. (Snowflake a plus) Experience with Data warehousing and database technologies Solid machine learning experience (modelling to deployment) Cloud exposure (GCP/AWS/Azure) Excellent More ❯

not only technically strong, but solution-oriented, strategically minded, and able to communicate insights clearly to both technical and non-technical audiences. Requirements: Advance Python (Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch) & SQL skills. (Snowflake a plus) Experience with Data warehousing and database technologies Solid machine learning experience (modelling to deployment) Cloud exposure (GCP/AWS/Azure) Excellent More ❯

/PhD in Computer Science, Data Science, Mathematics, or related field. 5+ years of experience in ML modeling, ranking, or recommendation systems . Proficiency in Python, SQL, Spark, PySpark, TensorFlow . Strong knowledge of LLM algorithms and training techniques . Experience deploying models in production environments. Nice to Have: Experience in GenAI/LLMs Familiarity with distributed computing tools More ❯

data mining; additional experience and knowledge of Big Data tools preferred. Excellent Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc). Understanding of Gen AI models, Vector databases, Agents, and follow the market More ❯

data mining; additional experience and knowledge of Big Data tools preferred. Excellent Python programming skills, including experience with relevant analytical and machine learning libraries (e.g., pandas, polars, numpy, sklearn, TensorFlow/Keras, PyTorch, etc.), in addition to visualisation and API libraries (matplotlib, plotly, streamlit, Flask, etc). Understanding of Gen AI models, Vector databases, Agents, and follow the market More ❯

processing data from diverse sources; exposure to Apache Spark or Hadoop is beneficial. Cloud Platforms : Proficiency with AWS , GCP , or Azure . ML Frameworks : Hands-on with scikit-learn , TensorFlow , PyTorch , or related libraries. More ❯

Data Scientist 's working across a variety of companies & industries Provide hands-on coding sessions and real-world project mentorship using Python and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch etc) Incorporate cloud-based tools and services (e.g., AWS, GCP, or Azure) into training to simulate modern ML engineering environments (Cloud Training can be provided to you) Keep More ❯

Data Scientist 's working across a variety of companies & industries Provide hands-on coding sessions and real-world project mentorship using Python and relevant ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch etc) Incorporate cloud-based tools and services (e.g., AWS, GCP, or Azure) into training to simulate modern ML engineering environments (Cloud Training can be provided to you) Keep More ❯

Mathematics, or a related field. Proven expertise in AI/ML fields such as LLMs, Computer Vision, Generative AI, NLP, or foundational models. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with cloud-based computing platforms. Strong analytical, mathematical, and coding skills (e.g., Python, C++, or Java). First author in research publications in peer-reviewed conferences More ❯

if: You have 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 More ❯

understanding of key concepts in computer science (e.g. databases, software engineering practices, cloud computing - especially AWS) and data science (e.g. machine learning process) Excellent knowledge of Python including Pytorch, Tensorflow and SKLearn as well as initial knowledge of LangChain, RAGAS and CI/CD You can demonstrate a growth mindset in terms of picking up new challenges and transforming More ❯

understanding of key concepts in computer science (e.g. databases, software engineering practices, cloud computing - especially AWS) and data science (e.g. machine learning process) Excellent knowledge of Python including Pytorch, Tensorflow and SKLearn as well as initial knowledge of LangChain, RAGAS and CI/CD You can demonstrate a growth mindset in terms of picking up new challenges and transforming More ❯

Claude, Gemini, Llama, Falcon, Mistral. Model performance and optimization: Fine-tuning and optimizing LLMs for quality, latency, sustainability, and cost. Programming and NLP tools: Advanced Python, frameworks like PyTorch, TensorFlow, Hugging Face, LangChain. MLOps and deployment: Docker, Kubernetes, Azure ML Studio, MLFlow. Cloud and AI infrastructure: Experience with Azure Cloud for scalable deployment. Databases and data platforms: SQL, NoSQL More ❯

LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade machine learning deployments on AWS, Azure, or GCP Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike More ❯

and data-driven features into production. Your Profile: Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong More ❯

and data-driven features into production. Your Profile: Experience in both data engineering and machine learning, with a strong portfolio of relevant projects. Proficiency in Python with libraries like TensorFlow, PyTorch, or Scikit-learn for ML, and Pandas, PySpark, or similar for data processing. Experience designing and orchestrating data pipelines with tools like Apache Airflow, Spark, or Kafka. Strong More ❯

streaming responses and cancelable endpoints to support real-time and interactive use cases. Preferred skills and experience Machine learning experience : Familiarity with machine learning frameworks and libraries such as TensorFlow, PyTorch, or scikit-learn. Natural Language Processing (NLP) : Experience with NLP techniques and tools, such as spaCy or NLTK. Distributed systems : Knowledge of distributed systems and experience with tools More ❯

Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

Advanced experience with SQL for data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions, including Snowflake. Understanding of personalization More ❯

research Deep understanding of AI/ML concepts applied to Digital Twin, with extensive hands-on experience in training and fine-tuning models using frameworks such as PyTorch or TensorFlow Proficiency with Generative AI tools such as GPT, DALL-E, Gemini, and a solid understanding of underlying Machine Learning pipelines Exceptional analytical and problem-solving skills Creative, flexible, and More ❯

traditional machine learning, deep learning and generative AI methods for both supervised, self-supervised and unsupervised learning with an emphasis on vision. Proficiency with deep learning frameworks such as TensorFlow/PyTorch. Proficiency with Python and strong software development background. Experience with MLOps practices, including versioning, deployment, and monitoring of models highly desirable. Ability to communicate complex technical concepts More ❯

machine learning or engineering roles. Strong expertise in Machine Learning and Deep Learning, with exposure to Reinforcement Learning as a plus. Proficiency in Python and modern ML libraries (e.g., TensorFlow, PyTorch, JAX, or Keras). Experience with version control systems (GitHub, GitLab) and knowledge of clean, maintainable coding practices. Familiarity with CI/CD pipelines for automating ML workflows. More ❯

machine learning or engineering roles. Strong expertise in Machine Learning and Deep Learning, with exposure to Reinforcement Learning as a plus. Proficiency in Python and modern ML libraries (e.g., TensorFlow, PyTorch, JAX, or Keras). Experience with version control systems (GitHub, GitLab) and knowledge of clean, maintainable coding practices. Familiarity with CI/CD pipelines for automating ML workflows. More ❯