1 to 25 of 166 PyTorch Jobs in the UK excluding London

best practices. Diagnose and resolve complex technical challenges in big data and ML systems. About You You're fluent in Python and experienced with leading ML/AI frameworks (PyTorch, TensorFlow, Scikit-learn, Hugging Face). You have a solid background in MLOps: experiment tracking, CI/CD for ML, model versioning, deployment, and monitoring. You've built scalable backend More ❯

and advanced analytics to generate business value and change, including optimisation of production processes Great knowledge of Python and in particular, the classic Python Data Science stack (NumPy, pandas, PyTorch, scikit-learn, etc) is required; Familiarity with PySpark is also desirable. A cloud platform experience (e.g Azure, AWS, GCP), were using Azure in the team. Good SQL understanding in practice More ❯

and advanced analytics to generate business value and change, including optimisation of production processes Great knowledge of Python and in particular, the classic Python Data Science stack (NumPy, pandas, PyTorch, scikit-learn, etc) is required; Familiarity with PySpark is also desirable. A cloud platform experience (e.g Azure, AWS, GCP), were using Azure in the team. Good SQL understanding in practice More ❯

and advanced analytics to generate business value and change, including optimisation of production processes Great knowledge of Python and in particular, the classic Python Data Science stack (NumPy, pandas, PyTorch, scikit-learn, etc) is required; Familiarity with PySpark is also desirable. A cloud platform experience (e.g Azure, AWS, GCP), were using Azure in the team. Good SQL understanding in practice More ❯

real-world use cases Contribute to code reviews, testing, and documentation Required Skills & Experience Strong proficiency in Python and its AI/ML ecosystem (e.g. NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) Experience with machine learning model development , training, and deployment Familiarity with LLMs , NLP , or computer vision techniques Solid understanding of software engineering principles and version control (Git) Experience working More ❯

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 Beam/ More ❯

solutions Own critical platform components from conception through production deployment Who You Are Expert proficiency in Python, JavaScript, and modern web frameworks Deep experience with AI/ML frameworks (PyTorch, TensorFlow, Transformers, LangChain) Mastery of prompt engineering and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation More ❯

solutions Own critical platform components from conception through production deployment Who You Are Expert proficiency in Python, JavaScript, and modern web frameworks Deep experience with AI/ML frameworks (PyTorch, TensorFlow, Transformers, LangChain) Mastery of prompt engineering and fine-tuning Large Language Models Proficient in vector databases (Pinecone, Weaviate, Milvus) and embedding technologies Expert in building RAG (Retrieval-Augmented Generation More ❯

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) and deploying More ❯

familiarity with core ML concepts (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 SQL to large-scale distributed More ❯

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 environment with More ❯

and deliver on project objectives. Masters degree in a STEM field (maths, science, engineering etc.) or equivalent Strong programming skills in Python (e.g., NumPy, Pandas, scikit-learn, TensorFlow/PyTorch). Demonstrable experience in creating and developing Python libraries. Demonstrable experience designing, implementing and training machine learning models from scratch. Strong foundations in applied mathematics and physics, particularly in statistical More ❯

tooling: Git, Unix/Linux, Docker. Plus, a strong opinion on your IDE/editor of choice is welcome Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn. Commercial experience with these is not essential. Excellent communication skills; both in customer-facing and internal team communication. Knowledge of MLOps is not essential, but some awareness of More ❯

Science, Engineering, or a related field with a strong ML focus. Solid understanding of deep learning architectures (CNNs, RNNs, Transformers). Proficiency in Python and ML libraries like TensorFlow, PyTorch, and scikit-learn. Strong experience with AWS services and cloud-native development. A proactive, collaborative mindset with a passion for learning and innovation. Bonus Points For Familiarity with MLOps principles. More ❯

machine learning engineers, AI researchers, and data scientists, driving end-to-end AI solution delivery. Designing and implementing AI architectures across cloud and on-prem environments, leveraging tools like PyTorch, TensorFlow, and Hugging Face. Spearheading the development of production ready AI models, from foundational LLMs to custom built cognitive agents, solving real world business challenges. Establishing scalable ML/AI More ❯

tooling: Git, Unix/Linux, Docker. Plus, a strong opinion on your IDE/editor of choice is welcome Familiarity with modern machine learning tools, for instance TensorFlow, Keras, PyTorch or SKLearn. Commercial experience with these is not essential. Excellent communication skills; both in customer-facing and internal team communication. Knowledge of MLOps is not essential, but some awareness of More ❯

and GenAI technologies for specific scenarios, estimate the time, cost and outcomes of different solutions, and apply effective risk management to ensure prompt delivery Deep experience in Python and PyTorch/TensorFlow. Industry experience in building scalable, production-grade AI services and automated data pipelines with tools like Docker, Kubernetes, and cloud platforms (e.g., AWS). Proven ability to collaborate More ❯

building something new. You should have: 2+ years of experience in machine learning or data science roles Strong programming skills in Python and experience with ML libraries (e.g., TensorFlow, PyTorch, scikit-learn) Solid understanding of machine learning algorithms and statistical methods Experience working with cloud platforms (AWS, GCP, or Azure) Familiarity with MLOps tools and practices is a plus A More ❯

as Python, R, C, C++, or Java, and their associated security considerations. * Previous experience with ML, LLM, deep learning and data manipulation techniques, libraries, and frameworks such as TensorFlow, PyTorch, Jax, and scikit-learn is desirable. * Experience in implementing secure coding practices, DevOps, CI/CD pipelines and familiarity with secure software development life cycle (SDLC) methodologies. * Strong understanding of More ❯

programming skills in languages such as Python with a strong focus on code optimization, modular design, and efficiency. Deep expertise in developing, training, and deploying models using frameworks like PyTorch and TensorFlow. Demonstrated experience in building scalable, production-grade AI services and automated data pipelines with tools like Docker, Kubernetes, and cloud platforms (e.g., AWS). Strong understanding of data More ❯

programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

programmer in a short timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques, for example, supervised/unsupervised machine learning More ❯

programming skills in languages such as Python with a strong focus on code optimisation, modular design, and efficiency. Deep expertise in developing, training, and deploying models using frameworks like PyTorch and TensorFlow. Demonstrated experience in building scalable, production-grade AI services and automated data pipelines with tools like Docker, Kubernetes, and cloud platforms (e.g., AWS). Strong understanding of data More ❯

pipelines and big data engineering to productionizing classical ML and LLMs, you'll be at the core of AI infrastructure. Key Requirements: Expert in Python, ML/AI frameworks (PyTorch, TensorFlow, Hugging Face) Proven MLOps, big data, and backend/API development experience Deep understanding of NLP and LLMs Proficient with cloud platforms (AWS/GCP/Azure), Airflow, DBT More ❯

need from you: Strong background in NLP and AI, particularly with LLMs and RAG-based solutions Proficiency in Python and modern AI/ML libraries (e.g. HuggingFace, LangChain, TensorFlow, PyTorch) Experience with data exchange and storage frameworks (e.g. APIs, SQL, NoSQL, Parquet) Track record of delivering technical solutions in Agile environments Excellent communication skills and a collaborative mindset Beneficial, but More ❯