76 to 100 of 137 Natural Language Processing Jobs

science fundamentals and time-series forecasting. Machine Learning: Strong grasp of ML and deep learning algorithms (e.g. Logistic Regression, Random Forest, XGBoost, BERT, LSTM, NLP, Transfer Learning). Reasonable Adjustments: Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built ...

Nice to Have) Basic experience with Python (e.g., scripting, automation, or integration support) Understanding of AI integration concepts Experience consuming AI/LLM/NLP APIs or internal AI platforms Awareness of AI security and risk topics, including: Sensitive data and PII handling Prompt injection risks Hallucinations and output validation ...

building and leading teams of experienced ML engineers/scientists. Solid understanding of the fundamentals of statistics, optimization, and ML theory, focusing on NLP and/or Computer Vision algorithms. Hands‐on experience in implementing distributed/multi‐threaded/scalable applications (incl. frameworks such as Ray, Horovod, DeepSpeed, etc. ...

e.g., Jira) Hands-on experience with a range of databases and storage technologies: Elastic, Mongo, Cassandra, Redis, S3 Desirable Skills PyTorch, AI/ML, NLP, LLMs Experience with the Atlassian Suite, GitHub, Linux CLI Broader data handling and advanced model-driven development exposure To learn more, please do reach ...

e.g., Jira) Hands-on experience with a range of databases and storage technologies: Elastic, Mongo, Cassandra, Redis, S3 Desirable Skills PyTorch, AI/ML, NLP, LLMs Experience with the Atlassian Suite, GitHub, Linux CLI Broader data handling and advanced model-driven development exposure To learn more, please do reach ...

platforms, ML pipelines, and AI products Ensure solutions are scalable, reusable, secure, and cost effective Select appropriate ML/AI techniques (e.g., regression, classification, NLP, forecasting, clustering) 2. Data & Analytics Engineering Alignment Work closely with data engineers to define: Data models and schemas Data quality rules ETL/ELT pipelines … Establish best practices and reusable design patterns Your Profile Essential Skills/Knowledge/Experience Data Science & ML Supervised and unsupervised learning Time series, NLP, recommendation systems (as applicable) Programming Python (NumPy, Pandas, Scikit learn) Optional: R, SQL Data Platforms Relational & NoSQL databases Big data frameworks (Spark, Hive, Databricks) MLOps ...

will be required to go through MOD SC clearance. You will be involved in: Design, build, and optimise machine learning models, including NLP, computer vision, and predictive analytics Own the ML lifecycle from data preparation through training, evaluation, and deployment Implement and maintain MLOps workflows for continuous integration and delivery ...

quantitative field. Fluent Python programmer with experience in deep learning frameworks (e.g., PyTorch). Expert in core data science techniques (supervised/unsupervised, NLP) and developing innovative algorithms. Leadership mindset, mentoring teams and fostering collaboration. Commercially aware, able to translate complex business problems into practical, high-quality solutions. ...

using Infrastructure as Code, with a strong focus on automation, compliance and best practice. Develop and integrate AI applications and services, including classical ML, NLP, RAG and generative AI solutions. Support experimentation and proof-of-concept work through secure AI sandboxes and evaluation of emerging tools and platforms. Monitor, optimise ...

Machine Learning, AI, Data Science, Engineering and SaaS – ideally 3-4 years. Experience managing teams from technical standpoint including people management. Confident working with NLP, LLM type solutions. Strong technical background in Data, Development & Cloud – Python/Azure. High level stakeholder management skills. Must have recent FinTech or Finance experience. ...

services, or complex enterprise environments Right to work in the UK Desirable Skills & Experience Exposure to AI/ML concepts (e.g. machine learning models, NLP, RPA, decision intelligence) Experience with data platforms, cloud ecosystems, or analytics tools Knowledge of post-merger integration (PMI) frameworks Agile certifications (e.g. Scrum, SAFe ...

will be required to go through MOD SC clearance. You will be involved in: Design, build, and optimise machine learning models, including NLP, computer vision, and predictive analytics Own the ML lifecycle from data preparation through training, evaluation, and deployment Implement and maintain MLOps workflows for continuous integration and delivery ...

modern automation tooling. Exposure to testing data-driven applications and distributed systems. Desirable: Experience testing GenAI or LLM-based services. Background in supporting ML, NLP, or data-heavy engineering environments. Familiarity with validating prompt flows, LLM responses, or AI pipelines. Tech Environment: Python, JavaScript, Node.js Performance automation tools API testing ...

stack including Microsoft Fabric and Azure Basic understanding of data science principles such as machine learning, cluster analysis etc. as well as generative AI, NLP etc. Hays Specialist Recruitment Limited acts as an employment agency for permanent recruitment and employment business for the supply of temporary workers. By applying ...

users. Research team published at top AI venues with work adopted by leading universities and Fortune 500 R&D teams. Advisors include a leading NLP researcher and a Google DeepMind team lead. Flexibility. Fully remote, UK timezone. Regular offsites. We care about what you ship, not when you’re online. ...

users. Research team published at top AI venues with work adopted by leading universities and Fortune 500 R&D teams. Advisors include a leading NLP researcher and a Google DeepMind team lead. Flexibility. Fully remote, UK timezone. Regular offsites. We care about what you ship, not when you’re online. ...

relevant internships in machine learning, data engineering, or software engineering. You’ll contribute to designing, implementing, and deploying production-grade ML systems—ranging from NLP pipelines to model‐driven workflow automation. You’ll learn quickly, gain real ownership, and see your work make tangible business impact. What … willingness to learn quickly. Foundational skills Degree in a STEM field. PhD candidates: applied research involving ML, optimisation, simulation, statistics, numerical methods, NLP, or related areas. Bachelor’s/Master’s: 1–2 years of industry experience or relevant internships in ML, software engineering, or data engineering. Programming experience (especially ...

experience we consider to be essential for the role: Programming & Libraries: Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK). AI-driven SDLC: Claude Code, Codex. Experience using Cursor, Windsurf, Replit, and GitHub Copilot. Google Stitch. ...

Have Deep experience building production AI/ML systems - real systems with evaluation, iteration, and feedback loops Strong foundations in machine learning, NLP, automated reasoning, or related fields - with hands-on experience applying them to LLM-based applications and multi-agent architectures Strong software engineering skills in Python - you build ...

Azure, Amazon Web Services, or Google Cloud Platform Familiarity with SQL and database systems Basic knowledge of APIs and software development practices Exposure to NLP, computer vision, or deep learning concepts Relevant certifications in AI or cloud technologies Skills Machine Learning Fundamentals Python & AI Libraries Data Processing & Analysis Problem ...

engineering experience Python and ML frameworks Experience deploying models to production Financial services or data-heavy background Cloud ML tooling exposure Nice to Have NLP or time-series modelling MLOps experience Risk or fraud use cases Why Join Applied ML in production High-impact financial use cases Growing AI capability ...

competitor landscapes to inform product vision. What Were Looking For: Proven experience managing AI or ML-driven products. Strong understanding of generative AI, NLP, computer vision, or related technologies. Ability to convert complex technical concepts into business outcomes. Track record of launching AI products from concept to production. Excellent communication ...

Skills & Experience Proven experience delivering AI, automation, or digital transformation programmes Strong understanding of AI/ML and intelligent automation technologies (e.g. RPA, NLP) Excellent stakeholder management and communication skills Experience working in Agile delivery environments Ability to bridge the gap between technical teams and business leaders Clearance Active ...

follow problems end-to-end Excellent communication with technical and non-technical stakeholders Preferred Deploying LLMs, multimodal models, or generative AI systems Background in NLP, computer vision, or multi-agent systems Sales engineering, pre-sales, or customer success engineering in high-growth environments Experience working in domains with heavy operational ...

Responsibilities: Design and implement generative models using state-of-the-art architectures (e.g., GPT, DALL E, Stable Diffusion). Fine-tune and optimize large language models (LLMs) for specific tasks and domains. Build pipelines for data preprocessing, model training, evaluation, and deployment. Collaborate with product, design, and engineering teams … Abilities: Strong programming skills in Python and experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face Transformers). Solid understanding of deep learning, NLP, and generative modeling techniques . Experience with training and fine-tuning large-scale models . Familiarity with cloud platforms (AWS, GCP, Azure) and MLOps tools. ...