1 to 25 of 41 Remote PyTorch Jobs in England

engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud

engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud

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

classification, time-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

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 leading

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 leading

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. ? Ability

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 and communicate

Expertise: Knowledge of data science fundamentals, solution lifecycles, and MLOps methodology. Generative AI Knowledge: Experience with LLMs, neural networks, and recommendation systems. Frameworks: TensorFlow, PyTorch, Hugging Face, OpenAI tools. Certifications: Cloud or AI architecture certifications are a plus. About SDG Group and the Role 🚀 SDG Group is a global consulting

Expertise: Knowledge of data science fundamentals, solution lifecycles, and MLOps methodology. Generative AI Knowledge: Experience with LLMs, neural networks, and recommendation systems. Frameworks: TensorFlow, PyTorch, Hugging Face, OpenAI tools. Certifications: Cloud or AI architecture certifications are a plus. About SDG Group and the Role 🚀 SDG Group is a global consulting

/SNS, RabbitMQ). Knowledge of real-time streaming (Kafka Streams, Apache Flink, etc.). Exposure to big-data or machine-learning frameworks (TensorFlow, PyTorch, Hugging Face, LangChain). Experience with real-time streaming technologies (Kafka, Apache Storm). Understanding of infrastructure and DevOps (Terraform, Ansible, AWS, Kubernetes). Exposure

/SNS, RabbitMQ). Knowledge of real-time streaming (Kafka Streams, Apache Flink, etc.). Exposure to big-data or machine-learning frameworks (TensorFlow, PyTorch, Hugging Face, LangChain). Experience with real-time streaming technologies (Kafka, Apache Storm). Understanding of infrastructure and DevOps (Terraform, Ansible, AWS, Kubernetes). Exposure

Profile Experience: From 1 year in data science or analytics to Principal-level leadership Proficiency in Python, R, SQL , and familiarity with Spark, TensorFlow, PyTorch Strong knowledge of machine learning, statistical modeling, and data engineering principles Experience with cloud platforms (AWS, Azure, GCP) Excellent communication and client-facing skills Active

designing and deploying LLMs in production environments. Experience with multi-agent systems, orchestration, and workflow automation. Proficiency in Python, AI/ML frameworks (PyTorch, TensorFlow, or similar), and cloud platforms (AWS, GCP, Azure). This is a full-time, permanent role, with a flexible hybrid working arrangement: 2 days

years of experience in ML/AI, preferably in energy, industrial automation, or smart infrastructure. · Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn, and programming in Python, R, MATLAB, or C++. · Experience deploying ML models in production using MLOps principles. · Familiarity with cloud platforms

years of experience in ML/AI, preferably in energy, industrial automation, or smart infrastructure. · Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn, and programming in Python, R, MATLAB, or C++. · Experience deploying ML models in production using MLOps principles. · Familiarity with cloud platforms

with TypeScript, Node.js, and modern JavaScript frameworks for UI development. Strong hands-on experience with Python, SQL, and AI/ML frameworks (e.g., TensorFlow, PyTorch), particularly within financial workflows. To apply for this role please submit your CV or contact Dillon Blackburn on or at . Tenth Revolution Group

with TypeScript, Node.js, and modern JavaScript frameworks for UI development. Strong hands-on experience with Python, SQL, and AI/ML frameworks (e.g., TensorFlow, PyTorch), particularly within financial workflows. To apply for this role please submit your CV or contact Dillon Blackburn on (phone number removed) or at (url removed

/AI engineering experience, including 3+ years deploying generative AI (LLMs, fine-tuning, RAG) in production. Strong technical grounding in Python, TensorFlow/PyTorch, transformer architectures (GPT, Llama, Mistral), vector databases, and prompt engineering frameworks. Proven record of delivering measurable ROI from AI projects. Solid engineering fundamentals: version control

Work closely with product and domain experts to translate requirements into robust ML solutions. Who You Are Strong proficiency in Python and experience with PyTorch or TensorFlow . Practical experience with LangChain, LangGraph, AutoGen , or similar LLM/agent frameworks. Skilled in prompt engineering , integrating foundation model APIs

performance or real-time environments. Proficiency in programming languages such as Python, R, or Java. Experience with machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Strong understanding of data analysis, statistical methods, and predictive modelling. Excellent problem-solving skills and attention to detail. Effective communication and collaboration

following key skills: - Demonstrated experience delivering business impact and growth through ML solution design and development - Proven experience in Python, TensorFlow/PyTorch, and modern ML frameworks. - Strong background in data modelling, feature engineering, and model deployment. - Experience with SQL, cloud platforms (AWS/GCP/Azure), and API integration.