1 to 25 of 482 Remote/Hybrid LLM Jobs in the UK

frameworks such as LangChain, LlamaIndex, CrewAI, Autogen, Google ADK, or similar. Ability to design and implement RAG pipelines, agentic workflows, MCP and integration with LLM APIs (OpenAI, Anthropic, Hugging Face OR similar). Proficiency in CI/CD and containerisation: GitHub Actions, Azure DevOps, Docker, Kubernetes. Nice to haves: Familiarity ...

technical stakeholders and to articulate a clear technical vision that aligns with business goals. Technical Skills AI/GenAI/Agentic Platforms LLM & Agentic Frameworks: Deep expertise in building production‐grade agentic systems using GCP as primary (ADK, Vertex AI) Multi‐Agent Systems: Hands‐on experience designing and implementing multi ...

pure research or proof-of-concept work. Key Responsibilities Design, build, and deploy machine learning models into production environments Work with generative AI/LLM integration, including prompt engineering, fine-tuning, or RAG-based architectures Collaborate with data engineers and software developers to build robust data pipelines feeding ML systems ...

tools for insurance use cases.* Implement Retrieval‐Augmented Generation (RAG) patterns using structured and unstructured insurance data (policies, submissions, bordereaux, claims documents).* Build LLM‐based workflows and agents using platforms such as ChatGPT (OpenAI) and Claude (Anthropic).**Cloud & Platform Engineering (GCP)*** Develop and deploy AI solutions on Google ...

technical challenges unique to your sector and have delivered AI solutions within it. Typical projects include: Building AI agents and autonomous workflows Developing LLM-powered applications and copilots Designing enterprise RAG and knowledge retrieval platforms Production deployment of Generative AI solutions AI integration into enterprise products and business platforms Scaling ...

Cost Controls. Software Engineering: Clean Code (SOLID, DRY), DDD, modular architecture, TDD, code quality tools. · AI Engineering (Desirable): RAG pipelines, agentic workflows, MCP, LLM integration (OpenAI, Gemini or other models from Huggingface), LangSmith, TruLens. Agentic Frameworks (Desirable): LangChain, CrewAI, Autogen, OpenAI Agents, Google ADK. WHAT YOU'LL LOVE ABOUT WORKING ...

date on market trends, new opportunities, and the changing landscape of AI technologies Requirements Experience AI Orchestration & Development: Expert‐level experience building complex LLM‐powered systems and multi‐agent workflows using frameworks like LangGraph, LangChain, AutoGen, or ADK Artificial Intelligence & Machine Learning: Deep practical understanding of machine learning algorithms, natural ...

such as TensorFlow, PyTorch, and scikit-learn. Experience with modern AI frameworks such as FastAPI, LangChain, LlamaIndex, or similar technologies. Hands-on experience with LLM APIs, foundation models, embeddings, vector databases, RAG architectures, and agentic AI systems. Experience working with large-scale datasets using SQL, distributed processing platforms such ...

essential: - Strong general-purpose programming (most commonly Python) applied to AI/ML systems - Semantic search, vector/embedding infrastructure, and RAG pipeline construction - LLM orchestration and agentic frameworks (eg LangChain/LlamaIndex-style tooling, multi-agent patterns) - Prompt engineering and systematic evaluation/guardrail tooling (hallucination detection, safety testing ...

Design, build, and deploy AI‐powered applications (chatbots, copilots, document intelligence, decision‐support tools) for insurance use cases; implement Retrieval‐Augmented Generation patterns; build LLM‐based workflows and agents. Cloud & Platform Engineering (GCP): Deploy solutions on GCP leveraging Vertex AI, BigQuery, Cloud Run, secure API integrations; build scalable, resilient, compliant ...

Generative AI use cases. Design and build APIs and microservices that integrate AI capabilities into internal and external workflows. Develop, fine‐tune, and deploy LLM‐based applications (e.g., chatbots, assistants, knowledge extraction tools). Architect scalable AI infrastructure on cloud platforms (AWS, GCP, or Azure), leveraging containerisation and IaC. Implement ...

deployment.Containerization and Orchestration: Hands-on experience with Docker, Kubernetes, and Docker Compose.Infrastructure as Code (IaC): Exposure to tools like Terraform.Monitoring Tools: Familiarity with LLM monitoring tools like Langsmith, Langfuse, or similar.CI/CD: Experience with continuous integration and deployment tools such as GitLab, GitHub, or Jenkins.Database ManagementVector Databases: Experience with ...

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 ...

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 ...

Points For Experience with agentic frameworks (e.g., LangChain, LlamaIndex) Experience in deep learning frameworks and front-end development Familiarity with Langfuse, Langsmith, or other LLM observability tools Understanding of Model Context Protocol and bias/hallucination mitigation techniques Previous success in integrating GenAI solutions into enterprise-scale systems Why Join ...

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 ...

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/ ...

speak with experienced AI Engineers, ML Engineers and MLOps Engineers. You'll be working on projects including: Building AI and agentic platforms Productionising LLM and GenAI solutions Developing ML platforms and feature stores Building RAG and vector search solutions Designing scalable data and AI pipelines Implementing MLOps and LLMOps best ...

Product Owners, and business stakeholders to understand requirements and translate them into practical technical solutions. Contribute to the design and implementation of GenAI and LLM-based solutions, including enterprise AI assistants, agentic workflows, AI automation, and secure access to foundation models. Build frameworks, templates, standards, and reference implementations that reduce ...

resilience. We INNOVATE with agentic and generative AI solutions– You will design, deliver and scale solutions for advanced AI systems, including agentic workflows, LLM-powered applications, and decision intelligence platforms. We AUTOMATE for speed and reliability – You will build and streamline data pipelines, infrastructure provisioning, and deployments using DevOps practices ...

embedding models. Develop AI agents and multi-agent workflows using frameworks like LangChain, LangGraph, Semantic Kernel, CrewAI, or AutoGen. Fine-tune, evaluate, and monitor LLM performance for business use cases. Integrate AI solutions with enterprise applications, APIs, databases, and cloud platforms. Implement prompt engineering strategies to improve response quality ...

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 ...