1 to 25 of 413 PyTorch Jobs in the UK excluding London

Define and implement end-to-end AI pipelines: data collection/cleaning, feature engineering, model training, validation, and inference. • Rapidly prototype novel models (e.g., neural networks, probabilistic models) using PyTorch, TensorFlow, JAX, or equivalent. • Productionize models in cloud/on-prem environments (AWS/GCP/Azure) with containerization (Docker/Kubernetes) and ensure low-latency, high-availability inference. 2. … to production). • Hands-on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. • Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, Hugging Face, etc.). • Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). • Experience designing scalable ML infrastructure on cloud platforms (AWS More ❯

discipline. Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related field. Strong programming skills in Python. Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn. Proficiency in data manipulation and analysis using tools such as Polars, Pandas, NumPy, or SQL. Solid understanding of algorithms, statistics, and data structures. Experience with cloud More ❯

Deep understanding of AI/ML algorithms, LLMs, and modern AI architectures. • Proficiency in Python, SQL and AI/ML frameworks such as Hugging Face, LangChain, scikit-learn, TensorFlow, PyTorch and Semantic Kernel etc. • Develop compelling data visualizations and dashboards to support storytelling and real-time insights. • Ensure compliance with data governance, model explainability, and ethical AI practices, including data More ❯

such as AWS SageMaker or Azure ML), and optimising models for efficient inference using formats like ONNX and TensorRT Proficiency in Python and ML/engineering frameworks such as PyTorch, TensorFlow (including Keras), Hugging Face (Transformers, Datasets) and scikit-learn, etc Experience with MLOps tools, including MLFlow, workflow orchestrators (Airflow, Metaflow, Perfect or similar), and containerisation (Docker) Strong knowledge of More ❯

augmentation for textual analysis, with an interest in learning more. Experience working with commonly used data science libraries and frameworks, e.g. Spacy, pandas, numpy, scikit-learn, Keras/TensorFlow, PyTorch, LangChain, Huggingface transformers etc. Familiar with both on-premises and cloud-based platforms (e.g. AWS). Working understanding of ML Ops workflows and ability to perform basic model deployment without More ❯

pipelines, APIs, dashboards, and visualization tools to support AI model insights. · Write clean, well-documented, and secure code using Python, C C#, or R. · Leverage libraries such as TensorFlow, PyTorch, and Scikit-learn to prototype and optimize AI models. · Communicate complex technical concepts clearly to both technical and non-technical stakeholders. · Stay up-to-date with emerging AI trends, threat … Processing, Generative AI, and Machine Learning algorithms. · Experienced in full-stack development with proficiency in Python, C C#, or R. · Familiarity with AI libraries and tools such as TensorFlow, PyTorch, and Scikit-learn. · Demonstrated ability to break down complex technical problems and communicate solutions effectively. · Excellent analytical, written, and verbal communication skills. · Certifications such as SANS, OffSec (OSCP/OSCE More ❯

to ensure smooth deployment of scalable solutions. Work closely with data scientists to deploy machine learning models into production environments. Optimise model inference performance (leveraging frameworks like TensorFlow or PyTorch for model serving) and implement monitoring to track model performance, accuracy, and reliability post-deployment. Keep up-to-date with the latest developments in Python, AI/ML technologies, and …/Machine Learning is a plus. Must have 8 years’ experience working as a Software Engineer on large software applications Proficient in many of the following technologies – Python, REST, PyTorch, TensorFlow, Docker, FastAPI, Selenium, React, TypeScript, Redux, GraphQL, Kafka, Apache Spark. Experience working with one or more of the following database systems – DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing … to ensure smooth deployment of scalable solutions. Work closely with data scientists to deploy machine learning models into production environments. Optimise model inference performance (leveraging frameworks like TensorFlow or PyTorch for model serving) and implement monitoring to track model performance, accuracy, and reliability post-deployment. Keep up-to-date with the latest developments in Python, AI/ML technologies, and More ❯

to ensure smooth deployment of scalable solutions. Work closely with data scientists to deploy machine learning models into production environments. Optimise model inference performance (leveraging frameworks like TensorFlow or PyTorch for model serving) and implement monitoring to track model performance, accuracy, and reliability post-deployment. Keep up-to-date with the latest developments in Python, AI/ML technologies, and …/Machine Learning is a plus. Must have 8 years’ experience working as a Software Engineer on large software applications Proficient in many of the following technologies – Python, REST, PyTorch, TensorFlow, Docker, FastAPI, Selenium, React, TypeScript, Redux, GraphQL, Kafka, Apache Spark. Experience working with one or more of the following database systems – DynamoDB, DocumentDB, MongoDB Demonstrated expertise in unit testing More ❯

/ML development and data science. Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). Proficiency in Python, R, or other relevant programming languages. Proficiency in working with large datasets, data wrangling, and data preprocessing. More ❯

monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform Working knowledge of one or more ML engineering frameworks (e.g. TensorFlow, PyTorch, Keras, Scikit-Learn) Working knowledge of object-oriented programming and unit testing in Python Working knowledge of application and information security principles and practices (e.g. OWASP for Machine Learning) Working … right to close roles early when the particular role and/or location has had sufficient applications. Nice-to-have skills Python SQL Numpy Pandas PySpark YAML Terraform TensorFlow PyTorch Keras Scikit-learn Unix Scripting Docker Kubernetes AWS S3 DynamoDB Portsmouth, England Work experience Machine Learning DevOps Languages English Seniority level Seniority level Not Applicable Employment type Employment type Full More ❯

Applied Maths, Computational Linguistics, or related fields (MSc/PhD preferred). Experience with computational linguistics is highly desirable. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, Scikit-learn. Experience deploying AI solutions using AWS, GCP, or Azure. Hands-on experience with MLOps, CI/CD, and model performance monitoring. Background in behavioural biometrics, human-computer 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 ❯

. Any candidates with exposure to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for ML, and model performance monitoring. More ❯

. Any candidates with exposure to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for ML, and model performance monitoring. More ❯

powered solutions. Key Responsibilities: Develop and optimize AI/ML models with a focus on Generative AI, embeddings, and vector search. Implement deep learning models using frameworks like TensorFlow, PyTorch, or JAX. Collaborate with data scientists to integrate AI models into production systems. Maintain and enhance AI pipelines, ensuring efficiency and scalability. Conduct research on the latest AI advancements and More ❯

MSc/PhD preferred). Exposure to computational linguistics is highly desirable, from both academic research and commercial perspectives. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS, GCP, or Azure. Hands-on experience with MLOps, CI/CD for ML, and model performance monitoring. More ❯

in Computer Science, AI, Data Science, or related fields . Expertise in large language models (LLMs) and prompt engineering for business applications. Familiarity with AI libraries and frameworks (TensorFlow, PyTorch, Keras). Experience deploying AI models in cloud computing environments (AWS, Azure, Google Cloud). Deep understanding of enterprise AI architecture, data engineering, and model optimization . Strong problem-solving More ❯

in Natural Language Processing (NLP) and Large Language Models (LLMs). Hands-on experience with machine learning and deep learning methods. Deep understanding of deep learning frameworks such as PyTorch or TensorFlow. Experience in advanced applied ML areas like GPU optimization, fine-tuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search). Ability to work independently on tasks and More ❯

on Linux, Go, or similar. Utilise Advanced Technical Stack: Leverage our technical stack, including Python, SQL, PostgreSQL, BigQuery, and libraries such as Pandas, NumPy, SciKit-Learn, XGBoost, Matplotlib, and Pytorch, to develop robust and scalable AI/ML solutions. Strong foundation in data structures, algorithms, and problem-solving, with a clear focus on efficiency and optimisation. Experience with architecting and More ❯

language processing (NLP) techniques and developing custom solutions using large language models (LLMs) for business use cases. Proficient in Python-based AI/ML development using frameworks like TensorFlow, PyTorch, and Scikit-learn. LLM Orchestration and Development Expertise in building LLM-powered applications using frameworks such as LangChain and LangGraph, including prompt engineering, fine-tuning, and workflow orchestration. Skilled in More ❯

Natural Language Processing (NLP) and Large Language Models (LLMs) Hands-on experience with machine learning and deep learning methods. Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow. Experience in advanced applied ML areas such as GPU optimization, fine tuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search). Ability to work on tasks and More ❯

Natural Language Processing (NLP) and Large Language Models (LLMs) Hands-on experience with machine learning and deep learning methods. Deep understanding and expertise in deep learning frameworks such as PyTorch or TensorFlow. Experience in advanced applied ML areas such as GPU optimization, fine tuning, embedding models, inferencing, prompt engineering, evaluation, RAG (Similarity Search). Ability to work on tasks and More ❯

Extensive prior experience exploring and testing large language model behaviour, prompting and building products with language models. Expert knowledge of Python and advanced ML/LLM frameworks (e.g., TensorFlow, PyTorch, LangChain, LlamaIndex, etc). Deep understanding of agentic AI concepts and frameworks (e.g., agentic design patterns, multi-agent systems, reinforcement learning) and their applications in healthcare. Previous experience of training More ❯

Lead Machine Learning Engineer, Associate Director 1 Get AI-powered advice on this job and more exclusive features. Fitch Group is currently seeking a Lead Machine Learning Engineer, Associate Director based out of our Manchester office. As a leading, global More ❯

monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based CI/CD pipelines and Terraform Working knowledge of one or more ML engineering frameworks (e.g. TensorFlow, PyTorch, Keras, Scikit-Learn) Working knowledge of object-oriented programming and unit testing in Python Working knowledge of application and information security principles and practices (e.g. OWASP for Machine Learning) Working … right to close roles early when the particular role and/or location has had sufficient applications. Nice-to-have skills Python SQL Numpy Pandas PySpark YAML Terraform TensorFlow PyTorch Keras Scikit-learn Unit Testing Python Scripting Docker Kubernetes AWS S3 DynamoDB Immingham, England Work experience Machine Learning DevOps Languages English Seniority level Seniority level Mid-Senior level Employment type More ❯