26 to 50 of 980 LLM Jobs in the UK

technology and transformation. Solid hands‐on technical skills – for example SQL and Python, data manipulation and engineering, and exposure to generative AI/LLM tools, machine learning or analytics platforms such as Power BI or Alteryx. Experience with using low code applications such as Microsoft Copilot Studio and Power Platform. ...

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 ...

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 ...

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 ...

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 ...

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+ ...

with multiple modern programming languages including Python, Golang, TypeScript and similar technologies in an AWS/Cloud environment. Experience in the use of GPT, LLM, RAG, Gen AI, Prompt Engineering related technologies Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming ...

Nano/Pro/1.5) for multimodal reasoning; integrate via Vertex AI; grounding, structured output, function‐calling. Azure OpenAI/GPT‐4.x for enterprise LLM services; model selection, limits, rate management, content filters/AI safety. Amazon Q for developer and ops assistance across AWS stacks. Cursor, GitHub Copilot, Devin ...

software, SaaS platforms, or technically demanding application domains (scientific computing, real-time systems, large-scale data) Exposure to integrating machine learning, generative AI, or LLM-based components into application features Experience mentoring less experienced engineers Energy industry experience is not required but is a strong plus. We welcome candidates from ...

technically complex or scientifically demanding application domains (distributed systems, scientific computing, real-time systems, large-scale data) Experience integrating machine learning, generative AI, or LLM-based components into production systems Experience contributing to open-source projects, or integrating open-source components at scale. Energy industry experience is not required ...

ReqID: FEQ327R501 Location: London Skills: Data Science, Machine Learning, AI, LLM, GenAI Mission As a Senior Specialist Solutions Architect (ML & AI), you will serve as the trusted technical ML and AI expert for Databricks customers and the Field Engineering organization. You will partner with Solution Architects to guide enterprise ...

commitment to clean coding standards (SOLID/DRY), modular design, and comprehensive unit/integration testing. Generative AI Deep Dive: Hands-on experience with LLM training cycles, parameter-efficient fine-tuning (PEFT), and sophisticated prompt engineering. Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton ...

technology architecture, 3–5 years focused on AI/ML architecture in large, complex enterprise environments. Hands‐on command of modern AI stack: LLM APIs and fine‐tuning, vector databases, RAG, embedding pipelines, prompt engineering, and agent orchestration frameworks (LangChain, AutoGen, or equivalents). Practical exposure to agentic AI architecture ...

platforms. Demonstrates experience of cloud platforms such as Azure and AWS, with experience supporting and maintaining cloud‐based infrastructure. Hands‐on experience working with LLM‐based solutions, including Retrieval‐Augmented Generation techniques and prompt engineering approaches. Hands‐on experience working with data processing frameworks and platforms, including batch and streaming ...

engineering domains. End-to-end exposure in model lifecycle development, including extensive experience training and deploying ML models in production environments. Deep knowledge of LLM APIs, prompt engineering, and conversational AI patterns. Experience in fine-tuning LLMs for domain-specific enterprise applications. Strong expertise in MLOps and LLMOps, ensuring scalable ...

platforms, self‐service golden paths and platform‐as‐a‐product to abstract away cloud complexity for engineering teams. Practical experience using AI agents/LLM‐based tooling to automate and optimise infrastructure work, and interest in designing infrastructure that AI agents can operate safely. Strong security engineering mindset with hands ...

large, multi‐team deliveries setting strategy, making sound architectural decisions and enabling teams to deliver at pace. Hands‐on experience with agentic AI: building LLM‐powered applications/agents, tool use, orchestration, retrieval (RAG), prompt and safety engineering, evaluation and telemetry. Proficient knowledge of cloud‐native engineering: containers (GKE/ ...

Prototype and help harden new modelling approaches, analytical tools, and platform features. Work across the design and build of our internal AI layer, including LLM integrations, MCP servers, and agentic tooling, to automate research workflows and deliver analytical insights within the client‐facing platform. Help with the evolution ...

with multiple modern programming languages including Python, Golang, TypeScript and similar technologies in an AWS/Cloud environment. Experience in the use of GPT, LLM, RAG, Gen AI, Prompt Engineering related technologies Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming ...

large, multi-team deliveries - setting strategy, making sound architectural decisions and enabling teams to deliver at pace.Hands-on experience with agentic AI: building LLM-powered applications/agents, tool use, orchestration, retrieval (RAG), prompt and safety engineering, evaluation and telemetry.Proficient knowledge of cloud-native engineering: containers (GKE/Cloud ...

guide successful implementation and handover. What you'll bring Proven experience architecting AI solutions in complex, enterprise environments especially involving Agentic, GenAI or LLM technologies. Strong working knowledge of cloud platforms (AWS, Azure, GCP) and their native AI tools and services. Background in consulting or business-facing solution design, with ...