1 to 25 of 73 TensorFlow Jobs in England

could be doing Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery

could be doing Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery

could be doing Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery

could be doing Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery

could be doing Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery

building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model

building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model

with geospatial libraries such as GeoPandas, Shapely, and GDAL Knowledge of PostgreSQL/PostGIS for spatial data management Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Experience working

experience developing or deploying AI models across domains such as natural language processing, computer vision, or predictive analytics Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Strong teamwork

Cloud, or Azure) and modern DevOps practices (Infrastructure-as-Code). Proficiency with Git, Unix/Linux, Docker. Familiarity with ML frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn (commercial experience not essential). Excellent communication skills for both internal and client-facing interactions. Awareness of MLOps

areas such as speech recognition, speech enhancement, audio analysis, text-to-speech synthesis, and natural language processing. Proficiency with machine learning frameworks such as TensorFlow or PyTorch. Solid understanding of software engineering principles, including version control (Git), CI/CD pipelines, and agile development methodologies. Excellent communication, collaboration

Spark â Experience with Java 2D graphics and 3D OpenGL programming. â Experience with scientific computing libraries and frameworks: o Python: NumPy, SciPy, Pandas, TensorFlow (for ML/AI) o C Java: CUDA (for GPU acceleration) o Angular or React o Microservice: Quarkus, Spring Boot, AWS API Gateway

Cloud, or Azure) and modern DevOps practices (Infrastructure-as-Code). Proficiency with Git, Unix/Linux, Docker. Familiarity with ML frameworks such as TensorFlow, PyTorch, Keras, or scikit-learn (commercial experience not essential). Excellent communication skills for both internal and client-facing interactions. Awareness of MLOps

emerging AI tech. What We’re Looking For 5+ years in data engineering, with team or project leadership experience. Advanced Python (Pandas, PyTorch, TensorFlow, Scikit-learn). Strong AWS/GCP , MySQL , and CI/CD experience; Docker/Kubernetes a plus. Excellent communication, organisation, and problem-solving skills.

series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from

evaluation. Proven research experience in audio/speech processing with publications in top-tier conferences/journals. Proficiency with ML frameworks such as TensorFlow or PyTorch. Strong analytical and problem-solving skills; ability to design and execute rigorous experiments. Excellent communication and teamwork skills. Desirable skills: Experience in audio

MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine learning and large language models (LLMs), encompassing deployment, monitoring, and retraining. Familiarity with software engineering guidelines: version control

language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch) ? Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks. ? Experience with ML Ops, including model deployment, monitoring, and retraining pipelines.

automation products Essential skills: Strong experience with Machine Learning, LLMs, NLP and Data Science Proven track record of building and deploying AI models (Python, TensorFlow, PyTorch, LangChain) Hands-on with cloud platforms (preferably Azure, or AWS, GCP) Good understanding of APIs, microservices, and data pipelines Able to prototype quickly

technical debt management Ability to establish coding standards and best practices across teams AI/ML Expertise Strong understanding of machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with LLM integration (OpenAI, Anthropic, open-source models) Knowledge of RAG architectures, prompt engineering, and vector databases (Pinecone, Weaviate) Experience with

with languages such as Java, C++, or Go. Familiarity with modern UI frameworks (TypeScript, Node.js). Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch) applied to financial datasets. Minimum 4 years in AI-focused roles within finance or technical consulting, with exposure to regulatory compliance. Deep understanding

with languages such as Java, C++, or Go. Familiarity with modern UI frameworks (TypeScript, Node.js). Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch) applied to financial datasets. Minimum 4 years in AI-focused roles within finance or technical consulting, with exposure to regulatory compliance. Deep understanding

strong problem-solving skills and attention to detail Desirable Experience with Python and familiarity with data science or machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) Interest or experience in signals, RF, or SDRs Knowledge of version control (Git), containerisation (Docker), or cloud platforms (AWS, Google Cloud) Academic

strong problem-solving skills and attention to detail Desirable Experience with Python and familiarity with data science or machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch) Interest or experience in signals, RF, or SDRs Knowledge of version control (Git), containerisation (Docker), or cloud platforms (AWS, Google Cloud) Academic

quantitative field Proficient programming skills (Python preferred); familiarity with core data science libraries (NumPy, Pandas, Scikit-Learn) Experience with deep learning tools (e.g. PyTorch, TensorFlow, or similar) Strong mathematical/statistical background Familiarity with a wide range of data science methods (e.g. ML, NLP, Bayesian inference, APIs, SQL) Experience