26 to 50 of 92 TensorFlow Jobs in the South East

models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g. More ❯

models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g. More ❯

models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g. More ❯

models in a distributed environment. LLMs: Strong understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g. More ❯

models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g. More ❯

models in a distributed environment. LLMs: Deep understanding of LLM architectures, pre and/or post-training, evaluations and inference. Programming languages: Excellent proficiency in Python using PyTorch/TensorFlow/JAX framework. Customer facing: Experience working directly with customers to solve applied problems, including product integration. Cloud-native technologies: Experience in developing and deploying in cloud platforms (e.g. More ❯

The AI Engineer will have proven experience developing and deploying machine learning models in a commercial or scientific environment.* Proficient in Python and familiar with key libraries such as TensorFlow, PyTorch, and scikit-learn.* Experience working with cloud platforms such as Azure, AWS, or GCP, ideally within a data engineering or MLOps context.* Strong understanding of data processing, feature More ❯

derivatives, and risk management principles, with experience applying ML in trading, pricing, or risk analytics contexts. Technical Proficiency - Expert in Python and familiar with ML frameworks such as PyTorch, TensorFlow, and JAX. Skilled in using tools like scikit-learn, XGBoost, and LightGBM. Data Engineering & Infrastructure Skills - Comfortable working with big data technologies (Spark, Dask), SQL/NoSQL databases, and More ❯

deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with More ❯

deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with More ❯

deep learning architectures. Strong understanding of two-tower neural networks, embedding techniques, and ranking models. 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. Experience with ML Ops, including model deployment, monitoring, and retraining pipelines. Ability to work cross-functionally with More ❯

Code (IaC), and cloud provisioning (OCI, AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting More ❯

Code (IaC), and cloud provisioning (OCI, AWS, GCP, or Azure). Solid understanding of CI/CD pipelines and automated testing frameworks. Experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn. Familiarity with MLflow, Kubeflow, DVC, or similar MLOps tools . Understanding of cloud security principles , IAM, and networking best practices. Proficiency in Python and Bash scripting More ❯

advection–diffusion modelling Knowledge of probabilistic ML or hybrid modelling (combining physics and data-driven approaches) Familiarity with Bayesian inference, MCMC , or probabilistic programming frameworks (e.g., PyMC, Stan, or TensorFlow Probability) Operating data science/ML workloads at scale using tools such as Argo Workflows, Prefect or similar Experience working with geospatial data (e.g., GeoPandas, Shapely, Rasterio) Knowledge of More ❯

advection–diffusion modelling Knowledge of probabilistic ML or hybrid modelling (combining physics and data-driven approaches) Familiarity with Bayesian inference, MCMC , or probabilistic programming frameworks (e.g., PyMC, Stan, or TensorFlow Probability) Operating data science/ML workloads at scale using tools such as Argo Workflows, Prefect or similar Experience working with geospatial data (e.g., GeoPandas, Shapely, Rasterio) Knowledge of More ❯

AI Engineer Location: Weybridge, Surrey Remuneration: £40,000 - £50,000 depending on experience Leading the way in tech innovation, our client is seeking a dynamic AI Engineer to join their ambitious and forward-thinking team. This role offers an exciting More ❯

or Gaussian Splatting techniques. Desirable Skills Demonstrated experience in: Generative AI, including hands-on implementation of state-of-the-art models. 3-D vision Developing with machine learning frameworks – Tensorflow/Pytorch Model optimization and knowledge distillation. Strong fundamentals in machine learning, NLP and Computer Vision Publications in top ML/AI conferences/journals (e.g., ICML, NeurIPS, ICLR More ❯

or Gaussian Splatting techniques. Desirable Skills Demonstrated experience in: Generative AI, including hands-on implementation of state-of-the-art models. 3-D vision Developing with machine learning frameworks – Tensorflow/Pytorch Model optimization and knowledge distillation. Strong fundamentals in machine learning, NLP and Computer Vision Publications in top ML/AI conferences/journals (e.g., ICML, NeurIPS, ICLR More ❯

agency, consultancy, or fast-paced business. Strong programming skills in Python, with experience building automation workflows, APIs, and data pipelines. Exposure to AI/ML frameworks (e.g. OpenAI, LangChain, TensorFlow, PyTorch). Solid knowledge of SQL and data warehousing concepts. Demonstrated experience with cloud platforms ( GCP & Azure preferred). Strong understanding of data modelling, transformation, and orchestration (e.g. dbt More ❯

agency, consultancy, or fast-paced business. Strong programming skills in Python, with experience building automation workflows, APIs, and data pipelines. Exposure to AI/ML frameworks (e.g. OpenAI, LangChain, TensorFlow, PyTorch). Solid knowledge of SQL and data warehousing concepts. Demonstrated experience with cloud platforms ( GCP & Azure preferred). Strong understanding of data modelling, transformation, and orchestration (e.g. dbt More ❯

agency, consultancy, or fast-paced business. Strong programming skills in Python, with experience building automation workflows, APIs, and data pipelines. Exposure to AI/ML frameworks (e.g. OpenAI, LangChain, TensorFlow, PyTorch). Solid knowledge of SQL and data warehousing concepts. Demonstrated experience with cloud platforms ( GCP & Azure preferred). Strong understanding of data modelling, transformation, and orchestration (e.g. dbt More ❯

ML or GenAI platforms end-to-end. Hands-on expertise with Google Gemini and associated GCP AI/ML tools. Strong programming ability in Python, with frameworks such as TensorFlow and PyTorch. Experience migrating models from Dataiku (or similar platforms) into Google Cloud environments. Knowledge of MLOps, model monitoring, data drift, and concept drift. Understanding of AI bias mitigation More ❯

Learning (policy optimisation, reward modelling, 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/TPUs. If this sounds More ❯

Learning (policy optimisation, reward modelling, 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/TPUs. If this sounds More ❯

Learning (policy optimisation, reward modelling, 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/TPUs. If this sounds More ❯