1 to 25 of 871 Permanent LLM Jobs in the UK

systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep More ❯

systems into the wider enterprise architecture. Technical Skillset Requirements Core AI & Frameworks: Agentic Frameworks: Expert-level knowledge of agentic frameworks such as LangChain, LangGraph, Google Agent Development Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep More ❯

Infrastructure & cloud : Familiarity with cloud platforms (Azure preferred; AWS or GCP also valuable), containerization (Docker, Kubernetes), and infrastructure-as-code tools like Terraform. Applied AI development: Experience working with LLM APIs (e.g., OpenAI) and building lightweight AI agents. Familiarity with orchestration tools like Temporal is a plus. Collaboration and impact: Strong problem-solving ability, intellectual curiosity, and a pragmatic approach More ❯

Infrastructure & cloud : Familiarity with cloud platforms (Azure preferred; AWS or GCP also valuable), containerization (Docker, Kubernetes), and infrastructure-as-code tools like Terraform. Applied AI development: Experience working with LLM APIs (e.g., OpenAI) and building lightweight AI agents. Familiarity with orchestration tools like Temporal is a plus. Collaboration and impact: Strong problem-solving ability, intellectual curiosity, and a pragmatic approach 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 ❯

experts to deliver AI capabilities that are both powerful and trustworthy. Responsibilities Design, train, and deploy Generative AI and NLP models for document understanding, summarization, and generation. Work with LLM APIs (e.g., OpenAI, Anthropic, Mistral) and open-source models (e.g., Llama, Falcon, Mixtral) for custom workflows. Build scalable pipelines and services that integrate GenAI into production systems. Fine-tune and More ❯

AI systems. Lead the end-to-end development of AI solutions: design, build, test, and deploy models that are robust, scalable, and used by real users. Apply NLP and LLM techniques to solve real-world problems, ensuring models are optimized for performance and reliability. Continuously improve model quality through tuning, evaluation, and feedback from production usage. Evaluate third-party AI More ❯

in non-technical terms to business users. High proficiency in MS Office, particularly PowerPoint and Excel Experience in Retail and consumer goods is highly advantageous. Experience integrating GenAI/LLM solutions into enterprise workflows. Experience in consulting (including internal consultancies) is highly advantageous. Professional fluency in English required. More ❯

Create and manage high-quality vector embeddings for semantic search, text classification, and other NLP tasks. You will work extensively with vector databases like Pinecone, Weaviate, or Chroma. Construct LLM Chains and Graphs: Utilize LangChain or LangGraph to develop, prototype, and productionize complex, stateful applications and workflows powered by LLMs. Model Integration & Deployment: Fine-tune, evaluate, and deploy LLMs and More ❯

Degree in Statistics, Computer Science, or a related field. - 5+ years in data science with at least 2 years leading teams. - Proven success in production deployment of ML/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit More ❯

Degree in Statistics, Computer Science, or a related field. - 5+ years in data science with at least 2 years leading teams. - Proven success in production deployment of ML/LLM/NLP/CV models. - Strong understanding of machine learning fundamentals, statistical inference, and model evaluation. - Advanced proficiency in SQL (e.g., PostgreSQL, ELT/ETL) and Python (PyTorch, LightGBM, Scikit More ❯

ready applications that drive automation, optimisation, and intelligence across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines More ❯

ready applications that drive automation, optimisation, and intelligence across multiple industries. What You’ll Be Doing: Designing, developing, and deploying machine learning and AI models Designing, developing, and deploying LLM applications (e.g. GPT, LLaMA, Claude) integrated with RAG pipelines Implementing end-to-end workflows: from data acquisition, cleaning, and feature engineering to model training, deployment, and monitoring Building scalable pipelines More ❯

AI orchestration and agentic reasoning What We’re Looking For Proven experience with LangGraph (bonus: contributions to the open-source project) Strong background in Python, LangChain, OpenAI APIs, and LLM architectures Familiarity with vector databases, retrieval-augmented generation (RAG), and prompt engineering Understanding of software design principles, version control (Git), and CI/CD practices Creative problem-solver with a More ❯

AI orchestration and agentic reasoning What We’re Looking For Proven experience with LangGraph (bonus: contributions to the open-source project) Strong background in Python, LangChain, OpenAI APIs, and LLM architectures Familiarity with vector databases, retrieval-augmented generation (RAG), and prompt engineering Understanding of software design principles, version control (Git), and CI/CD practices Creative problem-solver with a 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 ❯

an agile or fast-paced tech environment with exposure to AI/ML pipelines. Experience in a managed services or vendor-driven environment. Familiarity with prompt engineering and large-language-model assisted workflows to optimise annotation and validation processes. In-depth knowledge of ethical AI practices and compliance frameworks. More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and 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 ❯

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 ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and More ❯

of API design, data modelling, and microservice architecture. Excellent communication skills, with the ability to translate technical outcomes into business impact. Tech Environment Languages: Python, TypeScript, Java AI/LLM: OpenAI, Anthropic, Retrieval-Augmented Generation (RAG) Infrastructure: AWS (Lambda, ECS, S3), Terraform, Docker Databases: PostgreSQL, MySQL, Redis, vector databases DevOps: GitHub, CI/CD pipelines Why Join Competitive salary and More ❯