1 to 25 of 309 DemandTrendPermanent 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 ❯

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 ❯

tuning, RAG, agents) 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

AI-powered solutions. Key Responsibilities: Develop and optimize AI/ML models with a focus on Generative AI, embeddings, and vector search. Implement deep learning models using frameworks like TensorFlow, PyTorch, or JAX. Collaborate with data scientists to integrate AI models into production systems. Maintain and enhance AI pipelines, ensuring efficiency and scalability. Conduct research on the latest AI More ❯

platforms, and retrieval-augmented generation systems. Qualifications: 3+ years of experience delivering full-stack AI/ML applications in production. Proficiency in Python, especially with ML libraries such as TensorFlow, PyTorch, scikit-learn, and backend frameworks like FastAPI, Django, Flask. Experience with frontend frameworks: React, Vue, or Angular. Hands-on experience building and scaling ML models (LLMs, NLP, classification More ❯

versioning Exposure to tools like KServe, Ray Serve, Triton, or vLLM is a big plus Bonus Points Experience with observability frameworks like Prometheus or OpenTelemetry Knowledge of ML libraries: TensorFlow, PyTorch, HuggingFace Exposure to Azure or GCP Passion for financial services Qualifications Degree in Computer Science, Engineering, Data Science, or similar What We Offer A collaborative and innovative work More ❯

. Experience with CI/CD pipelines and Docker containerisation for ML model development/deployment. Data preprocessing and feature engineering expertise. Programming skills in Python and ML frameworks (TensorFlow, Torch, scikit-learn). Statistical analysis and model evaluation capabilities. Previous experience in the banking sector is mandatory. Some other highly valued skills may include: Experience with experiment tracking More ❯

Generative AI application is highly desirable Strong expertise in Generative AI, LLMs, or related NLP technologies Proficiency in Python and/or Scala; experience with ML libraries such as TensorFlow, PyTorch, HuggingFace, or scikit-learn Experience with Databricks, distributed data systems (e.g., Spark, Hadoop), and cloud platforms (AWS, GCP, or Azure) Ability to thrive in ambiguous environments, working closely More ❯