1 to 25 of 35 TensorFlow Jobs in Central London

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 ...

commercial optimisation. Required Skills and Experience Technical Skills Proficiency in Python and associated data science libraries (NumPy, Pandas, SciPy, scikit‐learn, PyTorch/TensorFlow). Strong experience with time‐series modelling (ARIMA, Prophet, LSTMs or similar). Understanding of optimisation methods (linear, mixed‐integer, reinforcement learning desirable). Strong ...

Natural Language Processing (NLP), information extraction, and working with large language models (LLMs) Python programming and major ML frameworks such as PyTorch or TensorFlow MLOps practices including containerisation (Docker), orchestration (Kubernetes), and CI/CD pipelines tailored for ML workflows Utilising AI-enhanced development environments and tools to streamline ...

ethics and bias mitigation, with experience in implementing strategies to ensure air and unbiased AI solutions. • Serverless Computing and Distributed Systems • Deep Learning Frameworks (TensorFlow, PyTorch) • Innovation and Emerging Technology Trends • Strategic AI Vision and Roadmapping • Enthusiastic about working in a fast-paced environment using the latest technologies ...

Skills * 2-4 years in production-level AI/ML delivery. * Legal/professional services experience is a plus. * AI/ML frameworks: PyTorch, TensorFlow, LangChain. * Cloud: Azure (preferred), AWS, GCP. * MLOps: CI/CD, model lifecycle, monitoring. * Generative AI: LLMs, RAG, chat agents. * Data engineering alignment: ETL, governance. ...

years' experience in ML/AI engineering or data science roles (senior level). Hands-on experience with Python ML frameworks (scikit-learn, PyTorch, TensorFlow, XGBoost). Experience with AWS ML services (SageMaker, Lambda, S3) and MLOps/LLMOps tooling (MLflow, Weights & Biases, DVC). Proven track record transitioning ...

related field Strong software engineering skills: clean code, testing, debugging, version control, and system design Proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow, or JAX) Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes) Solid understanding of ML fundamentals (model training, evaluation ...

Essential experience includes: Commercial experience developing and deploying machine learning models in Python Proficiency with ML frameworks such as scikit-learn, XGBoost, PyTorch or TensorFlow Strong experience delivering ML solutions using AWS services (e.g. SageMaker, Lambda, S3) Expertise in experiment design, including hypothesis formulation, A/B testing ...

experiment tracking and monitoring Collaborate with data scientists and engineers What We're Looking For: Proficiency in Python and ML frameworks (scikit-learn, PyTorch, TensorFlow) Hands-on experience with AWS ML services (SageMaker, Lambda) Familiarity with containerisation (Docker) and orchestration (Kubernetes/ECS) Knowledge of LLMOps and GenAI tools ...

models together to achieve reliable results. True ML Fundamentals: You aren't limited to APIs. You have a background in training and evaluating models (TensorFlow/PyTorch/Keras/Scikit-learn) and know when a simple classifier is better than a generative model. Data Fluency: You understand ...

machine learning, deep learning, and optimisation Hands-on experience training, evaluating, and deploying ML models in real-world systems Proficiency with PyTorch (preferred) or TensorFlow; familiarity with the Hugging Face ecosystem Experience with audio or speech models and frameworks. Experience building and maintaining end-to-end ML pipelines ...

major cloud platforms- AWS, Azure, GCP Knowledge of GenAI technologies, including LLMs (Large Language Models), generative models, and related frameworks (e.g., LangChain, TensorFlow, etc.) Possess knowledge of cloud-based AI/ML services like AWS SageMaker, Azure Machine Learning, Google AI Platform, Databricks GenAI/BI, Snowflake GenAI ...

RLHF). Hands-on experience training/fine-tuning generative models (LLMs, diffusion, transformers, GANs). Strong knowledge of deep learning frameworks (PyTorch, JAX, TensorFlow). Proficiency in Python and standard ML libraries. Solid foundations in probability, optimisation, and statistics. Experience working with large-scale distributed training on GPUs ...

background (Bachelor’s degree 2:1 or above in a quantitative subject). Strong Python skills and experience with ML frameworks (e.g. scikit-learn, TensorFlow, PyTorch). Experience with model deployment, monitoring and ML pipelines (e.g. CI/CD, MLOps concepts). A collaborative mindset with strong communication skills ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

sports data (e.g., numerical, spatial, video, or metadata). Advanced Python expertise with strong hands-on use of ML/DL frameworks (e.g., PyTorch , TensorFlow), including taking models from experimentation into production model serving. End-to-end MLOps experience, including CI/CD for ML, experiment tracking, model registries ...

Requirements MSc or PhD in Machine Learning, Computer Science, Applied Math, or related field Strong Python engineering fundamentals Deep experience with PyTorch, JAX, or TensorFlow Hands‐on experience scaling ML systems in production environments Familiarity with MLOps tools (Weights & Biases, Ray, Docker, etc.) Experience with modern architectures: Transformers, Diffusion ...

series, dynamic forecasting, and complex sequential tasks. Industry experience implementing and refining forecasting systems in production. Proficiency in modern ML frameworks such as PyTorch, TensorFlow, or JAX. A track record of applying research results to real-world, high-impact problems. Highly Valued: Experience with real-time prediction systems, probabilistic ...