1 to 25 of 94 Natural Language Processing Jobs

technology sector Salary up to £130k + Equity Paddington based (4-5 days per week in-office) Tech environment: Python | PyTorch | TensorFlow | scikit-learn | NLP | LLMs | Data Engineering If you wish to keep your CV/data private, feel free to WhatsApp your details/… Design, develop, and deploy end-to-end ML systems , from concept to production. Build models for intelligent automation, natural language processing (NLP), and data-driven insights. Work with Python , PyTorch , TensorFlow , and scikit-learn to prototype and scale solutions. Partner with senior leadership to define

time and resources to explore, learn, and publish. What We’re Looking For 0–3 years of experience in Machine Learning, Data Science, or NLP/LLM. Strong Python skills; exposure to PyTorch/TensorFlow/Hugging Face. (Bonus) understand fundamentals of deep learning, LLMs, and MLOps, vector databases, embeddings … join an early-stage company where their work genuinely moves the needle. Machine Learning Engineer, LLM Engineer, AI Engineer, Artificial Intelligence, Deep Learning, NLP, Natural Language Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python

handling, and writing clean, maintainable code. Experience with key libraries including Pandas, NumPy for data manipulation, requests and APIs for integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/… frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language

handling, and writing clean, maintainable code. Experience with key libraries including Pandas, NumPy for data manipulation, requests and APIs for integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/… frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering, and MLOps practices for production deployment. Generative AI & LLM Integration - Proficient in working with Large Language

Expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) and understanding of deep learning and natural language processing (NLP). • Strong understanding of big data platforms such as Hadoop, Apache Spark, and data lakes. • Hands-on experience with cloud platforms (Azure, AWS, or Google

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

/CD tooling to ensure seamless model delivery and monitoring. Apply advanced techniques in deep learning, natural language processing (NLP), and statistical modelling to extract insights and drive decision-making. Explore and evaluate Generative AI applications, including prompt engineering, fine-tuning, and safety alignment using tools like

levels of the organization. Cloud native (Azure) development experience is a plus. Exposure to Python programming, natural language processing (NLP), or other specialized AI domains is a plus. Ability to mentor other team members. Ability to work independently with minimal supervision. Ability to work with people from

Neo4J...) Data exchange and processing skills (e.g. ETL, ESB, API...) Development (e.g. Python) skills Big data technologies knowledge (e.g. Hadoop stack) Knowledge in NLP (Natural Language Processing) Knowledge in OCR (Object Character Recognition) Knowledge in Generative AI (Artificial Intelligence) would be advantageous Experience in containerisation technologies

Neo4J...) Data exchange and processing skills (e.g. ETL, ESB, API...) Development (e.g. Python) skills Big data technologies knowledge (e.g. Hadoop stack) Knowledge in NLP (Natural Language Processing) Knowledge in OCR (Object Character Recognition) Knowledge in Generative AI (Artificial Intelligence) would be advantageous Experience in containerisation technologies

Neo4J...) Data exchange and processing skills (e.g. ETL, ESB, API...) Development (e.g. Python) skills Big data technologies knowledge (e.g. Hadoop stack) Knowledge in NLP (Natural Language Processing) Knowledge in OCR (Object Character Recognition) Knowledge in Generative AI (Artificial Intelligence) would be advantageous Experience in containerisation technologies

alongside an appreciation for regulatory considerations unique to this sector. You possess a thorough understanding of artificial intelligence concepts such as machine learning or NLP without needing direct coding skills-enabling you to communicate effectively with technical experts while focusing on strategic outcomes. Your proven ability in programme management means … well as familiarity with regulatory frameworks governing financial institutions. Conversant in artificial intelligence concepts such as machine learning, natural language processing (NLP), automation technologies including generative AI or agentic AI-without requiring hands-on coding expertise. Extensive experience in programme management encompassing oversight of multiple concurrent workstreams

collaborate closely with a multi-disciplined and distributed work function, utilise machine/deep learning and Natural Language Processing (NLP) to integrate AI solutions into products and services. Key skills and experience required: Hands-on AI and ML Engineering experience Experience developing in Python

collaborate closely with a multi-disciplined and distributed work function, utilise machine/deep learning and Natural Language Processing (NLP) to integrate AI solutions into products and services. Key skills and experience required: Hands-on AI and ML Engineering experience Experience developing in Python

deep learning, and reinforcement learning. Demonstrated knowledge and experience using artificial intelligence and machine learning techniques within one or more of the following fields: natural language processing, image processing and computer vision, image and pattern recognition. Demonstrated knowledge and experience with large language models

driven solutions to support a mission critical project. This role offers the opportunity to work on cutting-edge initiatives applying Machine Learning, Natural Language Processing, and Large Language Models (LLMs) within a secure environment. Key Skills & Experience: Proven experience as a Data Scientist , ideally

with AWS and Azure platforms, including AI services (e.g., AWS SageMaker, Azure ML) Proven experience developing or deploying AI models across domains such as natural language processing, computer vision, or predictive analytics Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g.

techniques to geological and geospatial challenges, helping us unlock insights from complex subsurface data. Key Responsibilities Develop robust Python pipelines for data manipulation using NLP and Foundation Models Apply geospatial libraries and techniques to subsurface geological datasets Implement secure coding practices and manage version control using Git Work with cloud … Proficiency in Python, with a strong grasp of Python best practices Experience using Git for version control and collaboration Experience in use of ML, NLP and Foundation Models in ETL pipelines Knowledge of secure coding principles Familiarity with geospatial libraries such as GeoPandas, Shapely, and GDAL Knowledge of PostgreSQL/

improve customer engagement metrics Skills ? Relevant experience in Customer Marketing Data Science including applied statistics and machine learning techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc) ? Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit

hear from you. Candidates will have gained the following skills and experience through previous roles: Responsibilities Directing the data gathering, data mining, and data processing processes in huge volume; creating appropriate data models. Exploring, promoting, and implementing semantic data capabilities through Natural Language Processing, text analysis