1 to 25 of 270 LLM Jobs in the UK excluding London

AI Solutions Engineer AI Solutions, B2B, B2C, Azure, AI Foundry, Open-AI, Microsoft Copilot Studio, Machine Learning, Python, TensorFlow, PyTorch, scikit-learn, Large Language Models, LLM, Data preprocessing, REST API, Microservices architecture, MLOps, CI/CD for ML, Power-BI, Docker, Kubernetes, AI Ethics, Cloud Platforms, AWS, Google Cloud Platform, SQL, NoSQL, DevOps, Financial services, Regulatory environments Contract Type: Hybrid More ❯

are proficient in developing data pipelines for large, complex datasets using Python, SQL, NoSQL, and big data tools, preferably on AWS or Azure. You have hands-on experience with LLM operations and agentic AI tools such as GPT-5, LangChain, and vector databases. You understand MLOps, including deployment, monitoring, and CI/CD pipelines, and can effectively communicate complex technical More ❯

Ensure your agents comply with regulations (GDPR, financial compliance) and guard against bias, hallucinations, and misuse. What You Bring Technical Mastery: Strong skills in Python, JavaScript, or similar languages. LLM & Agent Expertise: Hands-on experience with LLMs (OpenAI, Anthropic, Mistral) and agent frameworks. Advanced Tools: Familiarity with vector databases, RAG pipelines, prompt engineering, REST APIs, cloud deployment, and containerization (Docker More ❯

Azure/GCP), CI/CD, Docker/Kubernetes; workflow orchestration (Airflow/Prefect); experiment tracking (MLflow/W&B). Machine Learning for time series/tabular data; LLM/agentic systems, retrieval pipelines, and evaluation/guardrails. Research driven, rigorous and curious; clear scientific writing with a publication mindset. Ability to communicate complex ideas to non-technical audiences More ❯

Docker, Kubernetes). This role involves building scalable AI/ML systems, deploying LLMs, and operationalizing models in production. Key Responsibilities: Design, develop, and deploy ML, Deep Learning, and LLM solutions. Implement scalable ML and data pipelines in Databricks (PySpark, Delta Lake, MLflow). Build automated MLOps pipelines with model tracking, CI/CD, and registry. Deploy and operationalize LLMs More ❯

a small, autonomous squad with full ownership across the stack, from scalable back-end services to intuitive front-end interfaces, collaborating closely with the AI platform team to bring LLM-powered features into core products. What you’ll be doing Architect and build scalable, secure AI-powered applications Design performant back-end services in C#/.NET, integrating with AI More ❯

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 with ASR or captioning More ❯

implement advanced AI solutions spanning traditional machine learning, Generative AI, and Agentic AI paradigms. This includes leading hands-on development of models and RAG pipelines to support enterprise-grade LLM applications. MLOps and LLMOps : Build and maintain scalable MLOps and LLMOps pipelines, enabling automation of model training, testing, deployment, and monitoring. Enable reproducibility, traceability, and performance optimization across all AI … RAG. Strong grasp of prompt engineering techniques, including zero-shot and few-shot prompting strategies with ability to enhance the performance of LLMs and agentic AI systems. Familiarity of LLM model evaluation techniques such as ROUGE, LLM-as-a-Judge and BERT. Deep knowledge of cloud-native AI platforms and tools, particularly Databricks, Azure, AWS, Docker. Awareness of regulatory and … compliance considerations in AI, especially within financial services and other regulated industries. Experience Hands-on experience with leading ML frameworks (e.g., PyTorch, TensorFlow) and LLM libraries (e.g., Hugging Face, LangChain/LangGraph, LlamaIndex). Practical experience implementing CI/CD pipelines using tools like GitHub Actions or Jenkins, and managing MLOps and LLMOps with MosiacAI, MLflow, Sagemaker or similar platforms. More ❯

Key Responsibilities Design, build, and deploy Python-based AI and ML applications . Research and develop new Generative AI techniques, including fine-tuning and prompt engineering. Develop and maintain LLM-assisted frameworks and automation pipelines. Write clean, maintainable, and well-documented code for scalable deployment. Build and implement testing and evaluation frameworks for AI models. Collaborate with data scientists, software More ❯

and retraining pipelines. Exposure to feature stores, registries, and experimentation frameworks. Familiarity with business-driven metrics and experience balancing ML performance with commercial goals. Experience with generative AI and LLM frameworks for fine tuning, evaluation, deployment and serving desirable. About Rightmove Our vision is to give everyone the belief they can make their move. We aim to make moving simpler More ❯

Deployment: Design, prototype, and deploy state-of-the-art Generative AI models (e.g., LLMs, Transformers, Diffusion models ) tailored for enterprise use cases. Application Development: Build, fine-tune, and deploy LLM-based applications such as advanced chatbots, summarization tools, document Q&A systems, and code assistants. Advanced Techniques: Apply sophisticated techniques like Prompt Engineering , Retrieval-Augmented Generation (RAG) , and context-aware More ❯

and drive student success. WHAT YOU WILL BE DOING: Design and develop AI-driven systems and tools that enhance the learning experience for students and educators. Build and deploy LLM-based solutions, integrating frameworks such as LangChain, AutoGen, and RAG pipelines. Develop and optimise AI agents capable of reasoning, planning, and automating complex learning or data workflows. Collaborate with cross More ❯

RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. More ❯

RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. More ❯

AI Engineer to join their AI unit. The organisation have been building commercially successful Data Science products for years, and are now pushing further into Generative AI with multiple LLM-based projects in production across the company. In this role you will be responsible for building and deploying production level ML models for a breadth of interesting commercial projects. The More ❯

an initial 6-month engagement, with the potential to extend or convert to full-time. The Role: You’ll work across the full development lifecycle on cutting-edge bespoke LLM technology , tackling complex challenges that are transforming the legal sector. Tech Stack: Python, FastAPI, MySQL/Redis, Microservices, TDD Cloud experience – ideally Azure or AWS Degree or PhD in Computer More ❯

s new, it's exciting and it's an opportunity to work with some incredible talent in this space. The role The role is building AI applications based on LLM and models such as GPT and BERT You'll make use of Python programming, Pyspark, tensorflow, HuggingFace, LangChain, RAG techniques, interfacing with diverse data sets. Cloud data platforms and a More ❯

bring experience in both AI development and systems engineering, including: Commercial experience building and deploying multi-agent systems Strong Python development skills for AI/ML applications Understanding of LLM orchestration , memory, and tool usage Familiarity with AWS core services , cloud security, and IaC (Terraform/CloudFormation) Proficiency with containerisation (Docker) and Kubernetes-based environments A passion for experimentation , research More ❯

exhibits good interpersonal skills. Able to prioritise, multi-task, and deliver at pace with an iterative mindset. Experience with modern data platforms such as Databricks or Snowflake is advantageous; LLM API experience is a bonus. Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience and potential. Discretionary bonus scheme that recognises your hard work More ❯

Business, or equivalent experience. 5+ years of professional experience (for senior roles, 7+), ideally in IT support, product operations, or digital assistant environments. Familiarity with Generative AI, NLP, or LLM-based technologies (e.g., ChatGPT, Azure OpenAI). Strong written and verbal communication skills across technical and non-technical audiences. Experience improving support processes and documentation at scale. Analytical mindset and More ❯

models — while leading a small, high-performing team. What You’ll Do Build and maintain data pipelines and cloud infrastructure (AWS/GCP). Develop AI-driven applications including LLM tools, RAG pipelines, and automation solutions. Lead on client communication , project delivery, and stakeholder alignment. Collaborate with Data Science and Analytics teams to improve data access, automation, and governance. Drive More ❯

production-ready solutions. Ideal Candidate Hands-on experience with AI/ML tools (e.g. NLP, predictive modelling, process automation). Familiarity with cloud platforms (AWS, Azure, or GCP). LLM experience Strong problem-solving and collaboration skills. Ability to communicate technical concepts clearly to non-technical audiences. Passionate about applying AI responsibly and effectively in business contexts. Benefits Hybrid working More ❯

production-ready solutions. Ideal Candidate Hands-on experience with AI/ML tools (e.g. NLP, predictive modelling, process automation). Familiarity with cloud platforms (AWS, Azure, or GCP). LLM experience Strong problem-solving and collaboration skills. Ability to communicate technical concepts clearly to non-technical audiences. Passionate about applying AI responsibly and effectively in business contexts. Benefits Hybrid working More ❯

ways of working across mixed client-consultancy teams. 💡 About You Proven experience as a Product Manager in AI, data, or enterprise technology environments. Hands-on exposure to AI MVPs, LLM applications, or applied ML use cases (e.g. RAG, copilots, generative search). Strong technical grounding in vector search, embeddings, APIs, data pipelines, and prompt design. Skilled in navigating complex enterprise More ❯

technical expertise with strategic client engagement. As a Solutions AI Architect, you will: Lead the design and delivery of AI and cloud-native architectures, including Generative AI, NLP, and LLM solutions. Act as a trusted advisor to senior stakeholders, guiding AI roadmaps and strategy. Translate complex business needs into scalable AI-driven solutions across public cloud, edge, and hybrid environments. More ❯