1 to 25 of 2,979 Permanent LLM Jobs in England

grade Generative AI applications, including:* Retrieval-Augmented Generation (RAG)* AI Agents and Agentic Workflows* Prompt Engineering and Prompt Optimization* Model Evaluation and Observability Frameworks* LLM Fine-tuning and Adaptation Techniques* Hands-on experience with Azure AI Foundry, Azure OpenAI Service, Model Context Protocol (MCP), and modern AI orchestration frameworks.* Strong ...

understanding of the distinction between Generative AI and Agentic AI, including their foundations, capabilities, and appropriate use cases. Strong understanding of AI, ML and LLM concepts, including prompt engineering, prompt grounding, iterative loop techniques, context windows, embeddings, RAG, agentic workflows. Proven ability to integrate AI capabilities both into low-code ...

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 ...

NoSQL databases.Familiarity with Agile development methodologies and modern software delivery practicesAI Fluency – Experience or familiarity with integrating AI-powered capabilities into software applications, including LLM APIs such as OpenAI, AWS Bedrock, Anthropic, or similar platforms. Understanding of concepts such as prompt engineering, retrieval-augmented generation (RAG), AI agents, and responsible ...

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 ...

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 ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

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 (e.g. LangChain/LlamaIndex‐style tooling, multi‐agent patterns) Prompt engineering and systematic evaluation/guardrail tooling (hallucination detection, safety testing ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real-time and batch AI workloads at enterprise scale • Implement advanced large language model applications using retrieval-augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration • Make key technical decisions while remaining accountable ...

encompassing:Compute & Services: Java, .NET, Python, Cloud-native microservicesData & Analytics: Databricks, Microsoft Fabric, AWS Glue, Athena, Event Streaming (e.g., Kafka)AI & ML: Azure OpenAI, LLM Integration patterns, Vector DatabasesIntegration & Storage: Azure Data Factory (ADF), SQL Server, Enterprise relational platformsInfrastructure: Azure, AWS, and Hybrid Cloud networksCandidates are not expected ...

tool-calling frameworks (e.g. LangGraph or equivalent), including familiarity with the Model Context Protocol (MCP)• Evaluation engineering (golden datasets, regression gates in CI, LLM-judge calibration)• Guardrail and AI-observability tooling (e.g. NeMo Guardrails, OpenTelemetry GenAI conventions, LangSmith, Braintrust)MLOps & LLMOps• Hands-on with MLOps platforms (Azure ML, Databricks, SageMaker ...

field, or demonstrable relevant experience. Additional desired skills Knowledge of vector databases and retrieval strategies. Experience with recommender systems and ranking models. Familiarity with LLM evaluation tools (e.g., RAGAS, TruLens, LangSmith, Arize). A working understanding of cloud networking and platform infrastructure. Experience in e‐commerce or retail environments. ...

agentic AI systems—tool use, orchestration, multi‐step reasoning, or agent frameworks (e.g. LangGraph, LangChain, CrewAI, AutoGen, or similar). Experience with GenAI/LLM systems in a production context (RAG, prompt orchestration, evaluation, guardrails, cost/latency optimisation). Exposure to MCP (Model Context Protocol) and tool‐based/ ...

applied experience Experienced in cloud development (AWS), with a focus on application design and development. Experience with AI/ML model integration, Prompt engineering, LLM APIs (OpenAI, Bedrock) and Generative AI Advanced in Java and Python Strong experience with cloud platforms like AWS, Azure, or Google Cloud Platform. Experience building ...

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 ...

learning.Proficiency in Python for data analysis, model development, and deployment.Strong experience with transformer modelsProficiency in Generative AI technologies, including utilizing LLMs via API access, LLM evaluation tools, and prompt engineering.Knowledge of various RAG pipelines and their practical implementation.Experience building Agentic RAG systems is strong requirement.Experience with AI agent management frameworks ...

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 ...

application programming interfaces, data pipelines, inference pipelines, and platform capabilities that support real‐time and batch AI workloads at enterprise scale Implement advanced large language model applications using retrieval‐augmented generation, prompt orchestration, evaluation frameworks, model optimisation, agentic workflows, and tool integration Make key technical decisions while remaining accountable ...

Solution Design: Design, build, and maintain scalable AI architectures and pipelines that support business requirements, ranging from predictive modelling to Generative AI applications. LLM Integration: Operationalise Large Language Models (e.g., GPT, Llama, Mistral) and NLP workflows (BERT, topic modelling) for production environments. Agentic & Multi-Agent Systems: Design and orchestrate autonomous ...

Django, Flask, or FastAPI Experience with API development, integration, and web services in production environments Hands‐on experience integrating AI or ML models, LLM APIs, or automation frameworks into applications Strong knowledge of database systems including SQL, NoSQL, and data storage patterns for AI applications Familiarity with cloud platforms such ...

productionising agentic AI systems—tool use, orchestration, multi-step reasoning, or agent frameworks (e.g. LangGraph, LangChain, CrewAI, AutoGen, or similar).Experience with GenAI/LLM systems in a production context (RAG, prompt orchestration, evaluation, guardrails, cost/latency optimisation).Exposure to MCP (Model Context Protocol) and tool-based/function ...