1 to 25 of 300 TensorFlow Jobs in the UK

related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on 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 ❯

degree in a STEM field: Statistics, Mathematics, Computer Science, Engineering, or similar. Programming proficiency: Strong experience with Python and SQL; familiarity with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Machine learning expertise: Hands-on experience in supervised and unsupervised learning, with exposure to NLP, computer vision, or deep learning a plus. Data visualisation: Proficiency in Tableau More ❯

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. PyTorch, TensorFlow). 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 More ❯

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. PyTorch, TensorFlow). 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 More ❯

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. PyTorch, TensorFlow). 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 More ❯

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. PyTorch, TensorFlow). 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 More ❯

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. PyTorch, TensorFlow). 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 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 ❯

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 ❯

with cloud infrastructure Experience with graph technology and/or algorithms Our technology stack Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad More ❯

insight generation-using both proprietary and third-party/foundational models Strong Python skills, with expertise in core data science and AI/ML libraries (pandas, NumPy, scikit-learn, TensorFlow, PyTorch, etc) Familiarity with additional programming languages such as Java, JavaScript/TypeScript, or C++ is beneficial, but your core expertise is in the Python ecosystem Skilled in core More ❯

programming and performance optimization Experience developing and deploying production machine learning applications on cloud platforms (GCP preferred, AWS and Azure acceptable) Familiarity with Python ML packages such as PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas Strong SQL skills for data preparation and feature engineering Knowledge of MLOps principles, including automated retraining, monitoring, and deployment strategies Basic understanding of 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 ❯

optimisation * Experience developing & deploying productionised Machine Learning applications on a cloud platform (GCP ideal, AWS & Azure also acceptable) * Experience with common Python packages for Machine Learning - examples include PyTorch, TensorFlow, XGBoost, scikit-learn, SciPy, NumPy, pandas, etc. * Strong knowledge of SQL and its use for data preparation & feature engineering * Understanding of & practical experience with implementing MLOps principals - including automated More ❯

the lifecycle of ML projects, including initial conceptualization, data handling, model development, and deployment. Proficiency in programming languages, including Python. Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, etc. Experience developing Python APIs using tools such as FastAPI. Knowledge of database technologies (SQL, MongoDB, Databricks) and data pipeline tools. Familiar with ML CI/ More ❯

Machine Learning. Familiarity with Azure Databricks is a plus. Solid background in machine learning algorithms, data pre-processing, feature engineering, and model evaluation. Experience with deep learning frameworks like TensorFlow or PyTorch is desirable. Proficiency in handling large datasets, experience with Azure Data Factory, Azure SQL Database, and Cosmos DB. Understanding of CI/CD pipelines, containerization (Docker, Kubernetes More ❯

Machine Learning. Familiarity with Azure Databricks is a plus. Solid background in machine learning algorithms, data pre-processing, feature engineering, and model evaluation. Experience with deep learning frameworks like TensorFlow or PyTorch is desirable. Proficiency in handling large datasets, experience with Azure Data Factory, Azure SQL Database, and Cosmos DB. Understanding of CI/CD pipelines, containerization (Docker, Kubernetes More ❯

advancements in AI/ML, LLMs, and GenAI technologies. To Be Successful in The Role, You Will Have Required Skills: Proficiency in Python and machine learning framework s (e.g., TensorFlow, PyTorch, Scikit-Learn). Hands-on experience with OpenAI APIs and Microsoft Azure AI services. Strong understanding of large language models, prompt engineering, and fine-tuning techniques. Experience with More ❯

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 ❯

AI/ML development and data science. Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (eg, TensorFlow, PyTorch) and data science libraries (eg, NumPy, pandas, scikit-learn). Proficiency in Python, R, or other relevant programming languages. Proficiency in working with large datasets, data wrangling, and More ❯

up to date with the latest advancements in deep learning, computer vision, and related technologies. Key Skills & Experience Strong hands on experience with Computer Vision frameworks (e.g., OpenCV, PyTorch, TensorFlow). Proficiency in Python and ML/DL libraries (NumPy, Pandas, scikit-learn). Proven track record of building and deploying ML models in production environments. Knowledge of MLOps More ❯

and techniques (supervised, unsupervised, reinforcement learning). Solid background in data preprocessing, wrangling, and feature engineering. Proficiency in Python (essential) and familiarity with relevant libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). Experience with prompt engineering and model evaluation. Deployment experience using Docker or other containerisation tools. Exposure to GPU-based environments for large-scale model training and tuning. More ❯

practices in machine learning. Optimization : Continuously improve machine learning infrastructure and production workflows. Strong technical foundation in machine learning and software engineering Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Experience with cloud platforms (AWS, GCP, Azure) Experience with CI/CD pipelines for machine learning (e.g., Vertex AI) Familiarity with data processing tools like Apache More ❯