1 to 25 of 31 PyTorch Jobs in the North of England

lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled in data More ❯

GenAI applications. The work you 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 and monitoring. • Apply advanced techniques in More ❯

on-prem setup Requirements: 25 years experience in ML engineering/MLOps roles Strong proficiency in Python and experience with relevant ML libraries/frameworks (pandas, numpy, sklearn, TensorFlow, PyTorch) Strong understanding and experience in implementing end-to-end ML pipelines (data, training, validation, serving) Experience with ML workflow orchestration tools (e.g., Airflow, Prefect, Kubeflow) and ML feature or data More ❯

AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in AI/ML/Data science leadership with measurable business impact. Degree in Computer More ❯

AI governance and risk Required Skills & Experience Production experience with Generative AI, LLMs, NLP, and RAG systems Strong knowledge of Knowledge Graphs and semantic technologies Proficient in Python, SQL, PyTorch/TensorFlow Experience with DevOps, CI/CD, DevSecOps and secure ML pipelines. Proven experience in AI/ML/Data science leadership with measurable business impact. Degree in Computer More ❯

managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able More ❯

managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able More ❯

managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able More ❯

managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able More ❯

managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able More ❯

managing containerised workloads (Docker, Kubernetes, Kubeflow, etc.) and integrating with CI/CD tools (Jenkins, GitHub Actions, GitLab CI). Familiarity with distributed computing frameworks (Spark, Ray, TensorFlow Distributed, PyTorch Distributed). Strong understanding of monitoring, logging, and observability for large-scale ML systems. Experience in cost optimisation for compute/GPU workloads. Excellent people leadership and communication skills, able More ❯

for: 3+ years of experience in a Data Science/Machine Learning/Engineering role Strong Python skills and experience writing production-grade code Familiarity with ML frameworks (TensorFlow, PyTorch, Keras, SKLearn) Proficiency with Git, Unix/Linux, Docker Cloud experience (AWS, Azure, or similar) Excellent communication skills UK citizen eligible for SC clearance Nice to have: MLOps experience Open More ❯

and ML research. What We’re Looking For MSc or PhD in Computer Science, Data Science, Mathematics, Physics, or a related field. Strong Python skills (pandas, NumPy, scikit-learn, PyTorch). Curiosity, rigour, and drive to improve model performance. Why Join Us Work on real applied LLMs from training to deployment. Collaborate with an ambitious, research-informed team. Shape the More ❯

to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI More ❯

to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI More ❯

to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI More ❯

to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI More ❯

to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI More ❯

to shape both the technical direction and the AI strategy. Expect to spend your time on: Building and productionising LLM-based features and GenAI workflows. Using frameworks like LangChain , PyTorch , and TensorFlow to bring models into production. Working with AWS Bedrock, SageMaker , or GCP Vertex AI to deliver scalable cloud-based systems. Implementing strong MLOps and LLMOps practices - from CI More ❯

early-career technologist seeking formal qualifications and impactful work Essential skills: Strong foundation in AI/ML (e.g. neural networks, NLP, transformers) Proficiency in Python and ML frameworks (e.g. PyTorch, TensorFlow) Familiarity with LLM APIs and open-source models (e.g. GPT, Claude, Whisper) Understanding of agentic AI frameworks (e.g. LangChain, AutoGen) A passionate, motivated and driven person Desirable skills: Experience More ❯

Overview The Senior Data Scientist is responsible for developing and implementing advanced data science models, machine learning algorithms, and predictive analytics solutions to drive business insights, product innovation, and operational efficiencies at IMS. This role plays a critical part in More ❯

Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of offline/online metrics to guide product strategy. Excellent communication and stakeholder management skills, with More ❯

Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of offline/online metrics to guide product strategy. Excellent communication and stakeholder management skills, with More ❯

Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of offline/online metrics to guide product strategy. Excellent communication and stakeholder management skills, with More ❯

Deep expertise in search and recommendation systems (e.g., semantic embeddings, learning-to-rank, personalisation algorithms). Hands-on experience with modern ML toolchains — Python, Spark, and frameworks such as PyTorch or TensorFlow. Strong grounding in experimental design, A/B testing, and the use of offline/online metrics to guide product strategy. Excellent communication and stakeholder management skills, with More ❯