101 to 125 of 1,548 Natural Language Processing Jobs in the UK

Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning … or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯

Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning … or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯

Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning … or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯

Experience with feature stores (e.g., Feast, Tecton). Knowledge of distributed training (e.g., Horovod, distributed PyTorch). Familiarity with big data tools (e.g., Spark, Hadoop, Beam). Understanding of NLP, computer vision, or time series analysis techniques. Knowledge of experiment tracking tools (e.g., MLflow, Weights & Biases). Experience with model explainability techniques (e.g., SHAP, LIME). Familiarity with reinforcement learning … or generative AI models. Tools & Technologies: Languages: Python, SQL (optionally: Scala, Java for large-scale systems) Data Processing: Pandas, NumPy, Apache Spark, Beam Model Serving: TensorFlow Serving, TorchServe, FastAPI, Flask Experiment Tracking & Monitoring: MLflow, Neptune.ai, Weights & Biases #J-18808-Ljbffr More ❯

end ML projects, from early experimentation to production deployment. Comfort with ambiguity, and the ability to shape, test, and validate product ideas in fast-moving environments. Deep expertise in NLP and GenAI, including Transformers, LLMs, RAG architectures, fine-tuning, and/or agent-based workflows. Hands-on experience with recommendation systems or search-based applications. Strong collaboration skills, with a … track record of working effectively across data science, engineering, product, and design. Experience processing and leveraging large, messy, unstructured datasets and making sense of them through ML. Excellent technical leadership, communication and mentorship skills, with the ability to bring others along on complex AI initiatives. Experience with modern software engineering and MLOps practices, including CI/CD, automated testing More ❯

industrial automation sectors, with expertise in system protection, automation, monitoring, and diagnostics. Strong foundation in AI/ML techniques, including supervised, unsupervised, and reinforcement learning, deep learning, and large language models (LLMs). Experience with ML frameworks such as TensorFlow, PyTorch, and scikit-learn. Hands-on experience deploying ML models in production environments using MLOps principles. Expertise in relevant … . Familiarity with GraphDB, MongoDB, SQL/NoSQL, and other DBMS technologies. Understanding of system automation, protection, and diagnostics in relevant sectors. Experience with deep learning algorithms, reinforcement learning, NLP, and computer vision in applicable domains. Excellent communication, organizational, and problem-solving skills, with a strong emphasis on teamwork, collaboration, and fostering inclusive environments. At GE Vernova - Grid Automation, you More ❯

ability to manipulate, query and visualise data and training/evaluation results (using e.g., Pandas, Matplotlib, Seaborn). Proven understanding of Information Extraction and Retrieval techniques. Proven understanding of NLP and large language models. Proven understanding of database concepts (differences between graph/relational/non-relational). Proven familiarity with cloud computing platforms (e.g., AWS, Azure, GCP), Azure More ❯

agency is seeking a Lead Data Scientist to develop and deploy end-to-end AI solutions. You'll work on machine learning models for retention, price optimisation, recommendation engines, NLP, and Computer Vision. Responsibilities: Design, develop, and productionize machine learning models across various applications. Work with Python (ideally production-level code) and other tools like SQL, Spark, and Databricks. Apply … clustering, classification, regression, time series modelling, NLP, and deep learning. Develop recommendation engines and leverage third-party data enhancements. Implement MLOps/DevOps practices in cloud environments (AWS, Azure, GCP). Ensure explainability, bias detection, and algorithmic fairness in models. Collaborate with stakeholders to translate business challenges into data-driven solutions. Requirements: MSc or PhD Degree in Computer Science, Artificial … Intelligence, Mathematics, Statistics or related fields. Experience in ML algorithms (XGBoost, clustering, regression) Expertise in Time Series, NLP, Computer Vision, MLOps Knowledge of AWS/Azure/GCP, CI/CD, and Agile development Ability to own solutions and manage stakeholders How to Apply: Please register your interest by sending your CV to Emily Burgess via the Apply link on More ❯

methodologies to specific use case needs such as ML, AI, and analytics. Excellent communication skills, both verbal and written. Proficiency in advanced analytics techniques and machine learning algorithms, including NLP and time-series analysis. Knowledge of ML Ops and DevOps frameworks. Experience with OpenAI Endpoints, Google Vertex, Google Model Garden, and Microsoft AI Models. Key Responsibilities: Lead Gen AI and More ❯

Ph.D. or Master's in Computer Science, Electrical Engineering, Statistics, Mathematics, or a related field. Proven expertise in AI/ML fields such as LLMs, Computer Vision, Generative AI, NLP, or foundational models. Experience with deep learning frameworks (e.g., TensorFlow, PyTorch) and familiarity with cloud-based computing platforms. Strong analytical, mathematical, and coding skills (e.g., Python, C++, or Java). More ❯

both supervised and unsupervised. Proficient programming skills with Python, including libraries such as NumPy, pandas, and scikit-learn, as well as R. Understanding and usage of the OpenAI API. NLP: tokenization, embeddings, sentiment analysis, basic transformers for text-heavy datasets. Experience with LLM & Prompt Engineering, including tools like LangChain, LangGraph, and Retrieval-Augmented Generation (RAG). Experience in anomaly detection More ❯

on experience in developing and implementing AI and Data cloud solutions. Experience working with Azure AI services (or AWS, GCP) and AI model deployment. Understanding of generative AI, LLMs, NLP, and machine learning frameworks (TensorFlow, PyTorch, Hugging Face, etc.). Understanding of the AI application lifecycle, including application prototyping, productionising and monitoring. Python/Go/R. Problem solving skills. More ❯

foundations of Machine Learning algorithms Proficient in Python Have a deep knowledge of a sufficiently broad area of technical specialism (e.g. Time Series, Combinatorial Optimisation, Reinforcement Learning, Bayesian Statistics, NLP etc.) Good knowledge in mining large & complex data sets using SQL and Spark. Preferred Will be curious, enjoy problem solving and have empathy for the problems you are challenged with More ❯

Your Role Placed at the heart of Gensler’s People + Process + Technology, the Global Design Technology Studio drives the firm’s digital transformation of our creative and project delivery practices throughout the globe. Our ultimate outcome is to More ❯

data science to provide insight into the nature and structure of our data and content, both published content and internal data sets, and lead on developing models to improve processing, access, understanding and use of that data. Working closely with the Subject Matter Experts, Product Managers, Software Engineers and Product Designers, you will play a key role in improving … and line of business data and information. This will require working with structured and unstructured data, a large corpus of scholarly articles, using programmatic techniques such as statistical analysis, natural language processing, information retrieval, and machine learning. You will also work with the rest of the team to turn your insights and software prototypes into production services … and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated ability to communicate complex technical findings clearly and effectively, both verbally and in writing More ❯

data science to provide insight into the nature and structure of our data and content, both published content and internal data sets, and lead on developing models to improve processing, access, understanding and use of that data. Working closely with the Subject Matter Experts, Product Managers, Software Engineers and Product Designers, you will play a key role in improving … and line of business data and information. This will require working with structured and unstructured data, a large corpus of scholarly articles, using programmatic techniques such as statistical analysis, natural language processing, information retrieval, and machine learning. You will also work with the rest of the team to turn your insights and software prototypes into production services … and SQL, and data analysis libraries (e.g., Pandas, NumPy, SciPy, Tidyverse). Strong knowledge of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn, NLTK). Experience with NLP techniques, such as named entity recognition (NER), topic modeling, semantic similarity, and knowledge graph construction. Demonstrated ability to communicate complex technical findings clearly and effectively, both verbally and in writing More ❯

globally, who thrive on new challenges. We are seeking an experienced Machine Learning Engineer or Data Scientist with technical expertise in Recommender Systems, Natural Language Processing (NLP), and Computer Vision, to join our growing team of multidisciplinary data science and ML experts. As a Senior Data Scientist, you will work closely with the technical and research teams … development experience through hands on coding Detailed knowledge of modern deep learning concepts, including but not limited to Generative AI (GenAI) models, FCN, CNN, RNN, Autoencoders, Transformers, and Large Language Models (LLM) Familiarity with version control practices (Git), containers, MLOps Experience with at least one cloud platform Experience in formulating analytical problems into actionable research and applying advanced machine More ❯

new insights, enable analytics, gain efficiencies and automation. This role is based in an AI/ML team that is already working on projects involving Generative AI, Information Retrieval, NLP/NER/RE, document classification, and has won awards and recognition for its work. The team's future projects will be in diverse areas, such as regulatory, clinical, legal … Design and develop RAG based applications LLM fine-tuning, including preparation of training sets from internal data Agent-based applications Evaluating use-case specific LLMs AI/ML Engineering NLP: Named Entity Recognition across a variety of unstructured data Evaluating and training BERT-like models such as GLiNER, NuNER for NER tasks Analysing trade-offs between these models and LLMs … for NLP tasks Relationship Extraction: Evaluating different models for use-case specific RE, such as ATG Document and text Classification Data Engineering Designing and implementing data pipelines for model training and inference Building scalable data processing systems Optimizing data workflows and storage solutions Implementing robust ETL processes Evaluate and integrate new technologies and models Cross-team collaboration, identifying innovations More ❯

services such as S3, Lambda, and SageMaker Experience with Generative AI Models , including building, fine-tuning, and deploying generative models Deep understanding of Natural Language Processing (NLP) techniques like transformer models, attention mechanisms, tokenization, and embeddings Experience mentoring or leading team members Strong communication skills, with the ability to explain technical concepts clearly to both technical and More ❯

raised by the existing and future anomaly detection systems Develop anomaly detection algorithms using supervised and unsupervised machine learning models or any other relevant data analytics techniques such as Natural Language Processing or graph/network analysis Analyse how the actors of our ecosystem of counterparts (clients, agents, beneficial owners, ...) interact using graph databases and network … Familiarity with network analytics. Knowledge of compliance-related domain (sanctions, transaction monitoring, KYC, fraud detection, ...) ideally in the financial industry. Previous exposure to generative AI and/or Natural Language Processing. Familiarity with cloud platforms, preferably Azure #J-18808-Ljbffr More ❯

helping them to build better products and brands, and deliver effective marketing to consumers. Our software, machine learning and AI are key to how we deliver impact, centred on: Natural Language Processing, GPT & other LLMs : unearthing trends, themes and other patterns from large text-based data sets, and deploying state-of-the-art AI to automate and … productionise machine learning models. Work closely with a great programme team – project lead, data scientists and analysts – and interface with client technology counterparts. Identify ways to improve data reliability, processing efficiency and quality of our data output. Deploy pipelines in cloud environments and develop as a cloud technologist, as our world becomes increasingly reliant on cloud technologies. Produce detailed More ❯

University of Cambridge. Join a team of passionate data scientists and data architects working together to overcome complex organisation-wide data and technology challenges. As a data scientist, your natural inquisitiveness will be welcomed, and you are encouraged to experiment, try new things, and be empowered to bring your solution ideas to the table! In an across-functional team … You will have a relevant STEM-related BSc degree or equivalent qualification, or other evidence of equivalent knowledge. Professional or research experience in some of the following: ML/NLP tools like PyTorch, Scikit-learn, Transformer architecture, and Neural Networks. Coding and software engineering in mainstream programming languages, such as Python. Statistical packages for data manipulation and in the development … models (e.g., R, SAS). Exploratory data analysis and manipulation of large datasets using SQL and Spark. Data cleansing, Data visualisation. Application of Generative AI, knowledge graphs, and Large Language Models. Have strong analytical, problem-solving, and critical thinking skills. Be a natural self-starter, confident at networking with cross-functional teams. Use working knowledge of standard data More ❯

data using Exploratory Data Analysis (EDA) and model interpretability tools such as SHAP and LIME. Undertake preprocessing of structured and unstructured data, including text processing and cleaning for NLP applications. Strategic Collaboration & Product Development Work with product managers and stakeholders to ensure AI solutions align with business needs and customer expectations. Contribute to the development of data products by … AWS, or Azure) and version control tools such as Git. Strong problem-solving and communication skills, capable of explaining technical concepts to non-technical audiences. Experience in developing Large Language … Models is a plus. Degree in Data Science, Computer Science, or a related field. Experience in productionizing machine learning models and working with APIs for data extraction. Experience with NLP including Text Classification, Sentiment Analysis, and Topic Modelling using tools like NLTK, BERT, and GCP NLP API. Fluent in English, both verbal and written What We Offer Competitive base salary More ❯

you be doing AI-Ready Data Development: Design, develop, and maintain high-quality AI-ready datasets, ensuring data integrity, usability, and scalability to support advanced Generative AI models. Data Processing: Contribute to data extraction, cleansing, and curation initiatives for diverse text and numerical datasets. Implement sophisticated metadata enrichment strategies to enhance data utility and accessibility for AI systems. Algorithm … Implementation & Optimization: Implement and optimize state-of-the-art algorithms and pipelines for efficient data processing, feature engineering, and data transformation tailored for LLM and GenAI applications. GenAI Application Development: Apply and integrate frameworks like LangChain and Hugging Face Transformers to build modular, scalable, and robust Generative AI data pipelines and applications. Prompt Engineering Application: Apply advanced prompt engineering … Skills and Experience Academic Background: Advanced degree in AI, statistics, mathematics, computer science, or a related field. Programming and Frameworks: Hands-on experience with Python, TensorFlow or PyTorch, and NLP libraries such as spaCy and Hugging Face. GenAI Tools: Practical experience with LangChain, Hugging Face Transformers, and embedding models for building GenAI applications. Prompt Engineering: Expertise in prompt engineering, including More ❯

of the agency's work. Minimum Qualifications: Proven experience in a data science or AI-related role, preferably within a creative or marketing agency. Deep understanding of machine learning, natural language processing, and other AI techniques. Strong data analysis skills, with experience using tools like SQL, Python, R, and data visualisation platforms. Experience with cloud computing platforms More ❯