26 to 50 of 1,182 LLM Jobs in England

Preferred qualifications, capabilities, and skills Experience with data, AWS and AIML engineering in commercial settings, preferably in financial sector Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector ...

Snowflake . Experience with CI/CD and version control tools (Git, TeamCity or similar). Understanding of AI governance, prompt engineering, and LLM frameworks. Desirable Experience supporting business-critical financial applications. Knowledge of trading, securities, or capital markets. What We're Looking For Excellent communication and stakeholder management skills. ...

Senior AI Engineer/AI Engineering Consultant/Generative AI Consultant/Senior Machine Learning Engineer/AI & ML Consultant/MLOps Engineer/LLM Engineer/AI Engineering Consultant/Senior AI Engineer/Generative AI Engineer/AI Solutions Architect/Data & AI Consultant/AI Solutions Consultant ...

taking AI prototypes to production in enterprise environments. Education: BS in Engineering, Computer Science or equivalent; advanced degree preferred. Agentic AI/LLMs: LLM orchestration, prompt engineering, context-window management and production guardrails; MCP, tool-augmented agents and RAG at enterprise scale. Enterprise integration: Integrating AI into enterprise systems ...

computer vision, geospatial data science or predictive modelling to engineering, infrastructure, environmental or asset management work. Knowledge of tools and methods such as LangChain, LLM orchestration, agentic tool use, segmentation foundation models, zero/few‐shot visual understanding Experience developing reusable internal AI platforms, foundation‐model services or automation capabilities ...

engagements using modern full stack technologies. Integrate Azure AI services (Azure OpenAI, Cognitive Services, Azure ML, AI Search) into production applications. Architect and build LLM-powered features, agentic workflows, and MCP (Model Context Protocol) integrations to a production standard. Work directly with clients to understand requirements, shape solutions, and translate ...

experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking and evaluation Experience with NLP techniques such as text classification, entity extraction, semantic similarity,summarisationand ...

experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking and evaluation Experience with NLP techniques such as text classification, entity extraction, semantic similarity,summarisationand ...

experience with machine learning frameworks and approaches, including supervised learning, unsupervised learning, feature engineering, modelselectionand model validation Practical experience building Generative AI and LLM-based solutions, including prompt engineering, embeddings, vector databases, RAG, grounding, reranking and evaluation Experience with NLP techniques such as text classification, entity extraction, semantic similarity,summarisationand ...

enterprise applications. Strong knowledge of Microsoft Azure, including Azure AI Foundry, Azure AI Services and cloud-native application development. Experience implementing AI solutions including LLM integration, prompt engineering, Retrieval-Augmented Generation (RAG) and vector databases. Experience working with SQL and NoSQL databases, with knowledge of platforms such as SQL Server ...

enterprise applications. Strong knowledge of Microsoft Azure, including Azure AI Foundry, Azure AI Services and cloud-native application development. Experience implementing AI solutions including LLM integration, prompt engineering, Retrieval-Augmented Generation (RAG) and vector databases. Experience working with SQL and NoSQL databases, with knowledge of platforms such as SQL Server ...

enterprise applications. Strong knowledge of Microsoft Azure, including Azure AI Foundry, Azure AI Services and cloud-native application development. Experience implementing AI solutions including LLM integration, prompt engineering, Retrieval-Augmented Generation (RAG) and vector databases. Experience working with SQL and NoSQL databases, with knowledge of platforms such as SQL Server ...

communication skills, both with teams and in client-facing engagements Interested in building AI applications, ranging from forecasting tools to image recognition applications and LLM-based chatbots and agents Proven ability to build machine learning models and pipelines using Python and common ML and DL libraries ( e.g. Pytorch, Tensorflow ) from ...

make myself available outside of normal working hours to suit your schedule. KEY SKILLS AI Software Engineer/Machine Learning Engineer/LLM Engineer/Python/AWS/Azure/Docker/Kubernetes/Generative AI/NLP/RAG/MLOps/UKSV DV/Outside IR35/ ...

APIs, tooling, and bespoke logic. Architecture & Standards: Architect robust, scalable, and compliant Agentic AI solutions across industries. Lead multi‐agent orchestration, RAG pipelines, and LLM/SLM integration design. Champion observability, telemetry, and auditable AI standards. Build and maintain reusable frameworks and asset libraries. Team & Product: Help build and guide ...

Systems, or a related field, or equivalent practical experience. Machine Learning: Expertise in time‐series forecasting, clustering, and correlation algorithms. Generative AI: Experience with LLM prompting, Retrieval‐Augmented Generation (RAG), and LangChain. Cloud Infrastructure: Deep understanding of AWS, Azure, or GCP infrastructure components. Additional Preferred Qualifications Experience with enterprise observability ...

taking AI prototypes to production in enterprise environments. Education: BS in Engineering, Computer Science or equivalent; advanced degree preferred. Agentic AI/LLMs: LLM orchestration, prompt engineering, context‐window management and production guardrails; MCP, tool‐augmented agents and RAG at enterprise scale. Enterprise integration: Integrating AI into enterprise systems ...

Systems, or a related field, or equivalent practical experience. Machine Learning: Expertise in time-series forecasting, clustering, and correlation algorithms. Generative AI: Experience with LLM prompting, Retrieval-Augmented Generation (RAG), and LangChain. Cloud Infrastructure: Deep understanding of AWS, Azure, or GCP infrastructure components Additional Preferred Qualifications: Experience with enterprise observability ...

platform partnerships (e.g., Google, Anthropic), third‐party tooling, and outsourced delivery models Qualifications: Technological 15+ years of experience in Technology - Required Generative AI & LLM Engineering: Deep, hands‐on expertise in large language models including model selection, fine‐tuning, prompt engineering, retrieval‐augmented generation (RAG), vector database design, and evaluation methodologies. ...

strategic AI platform partnerships (e.g., Google, Anthropic), third-party tooling, and outsourced delivery models## **Qualifications:****15+ years of experience in Technology - Required:*** Generative AI & LLM Engineering: Deep, hands-on expertise in large language models including model selection, fine-tuning, prompt engineering, retrieval-augmented generation (RAG), vector database design, and evaluation ...

working experience in a role such as Software Engineer, Application Developer, or related occupation. Preferred qualifications, capabilities, and skills Experience building on top of LLM APIs: agent frameworks, tool/function calling, retrieval, and writing evals to measure output quality. Prompt and context engineering as an applied discipline, including cost ...

enabled solutions. Cloud and modern data infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps ...

enabled solutions. Cloud and modern data infrastructure exposure, plus experience in Financial Services or another regulated industry, is essential. Tech skills include Python, SQL, LLM development (prompt engineering, fine-tuning, RAG), LangChain/LangGraph, agent frameworks, vector databases, FastAPI, and AWS, Azure, GCP or Databricks. CI/CD, MLOps ...

ensuring a reliability of 99.9999% and beyond. \n \n The Work itself: \n \n \n Build Agentic Solutions using world's most advanced LLM models from Anthropic, OpenAI and others \n \n Build Deep Learning solutions using AI/ML Platforms for real time transactions at a scale of 100K+ ...

digital future of fintech transactions and ensuring a reliability of 99.9999% and beyond. The Work itself Build Agentic Solutions using world’s most advanced LLM models from Anthropic, OpenAI and others Build Deep Learning solutions using AI/ML Platforms for real time transactions at a scale of 100K+ ...