151 to 175 of 348 Retrieval-Augmented Generation Jobs in England

Architect & Engineer AI Systems: You won't just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just \"feel\" smart. Build the Backend ...

models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong ...

models, Anthropic Claude, Azure OpenAI, and open-source alternatives (Llama, Mistral). Experience with prompt engineering, fine-tuning, RAG (Retrieval Augmented Generation) architectures, vector databases (Pinecone, ChromaDB, FAISS), embeddings, and building AI-powered automation solutions that leverage natural language understanding. Appian BPA Platform - Strong ...

will do Build fine-tune and deploy ML and LLM-based solutions Work hands-on with Hugging Face models and tooling Design and implement RAG pipelines using LangChain or similar frameworks Build agent-based workflows where appropriate Integrate AI services into existing backend and product platforms Own model performance latency ...

high-impact projects while building and leading a team from the ground up. Key Skills/Experience: Experience with LLMs, generative AI models, and RAG pipelines. Frameworks: OpenAI, Anthropic Claude, Gemini, and open-source models Agentic AI, Langgraph, Crew AI, Autogen Python Development (Hands-on coding) Must have deployed ...

design and deployment of on-premises, hybrid, and cloud-native AI solutions. GenAI Implementation: Hands-on deployment of Azure OpenAI, Vector Databases, and RAG patterns. Data Engineering: Oversee large-scale data processing using Python and PySpark to support AI model fine-tuning. Leadership: Manage technical delivery teams and deliver upskilling ...

design and deployment of on-premises, hybrid, and cloud-native AI solutions. GenAI Implementation: Hands-on deployment of Azure OpenAI, Vector Databases, and RAG patterns. Data Engineering: Oversee large-scale data processing using Python and PySpark to support AI model fine-tuning. Leadership: Manage technical delivery teams and deliver upskilling ...

tools, and enterprise connectors. Skills: 6–8 years in Software/application engineering, AI engineering, or applied data engineering. Strong experience with LLMs, embeddings, RAG, retrieval stacks, and vector stores. Hands-on experience in multi-agent systems, agent orchestration, or MCP-like tool patterns. Proficiency in Python, including ...

these will be fully funded. As a GenAI/Python Engineer , you’ll help design, build, and scale AI-powered applications using tools like RAG, intelligent agents, and LLMs. This hands-on, client-facing role blends technical depth with consulting impact. You will: Develop and scale GenAI solutions from POCs ...

strong parent-company backing. Key Responsibilities Design and deploy AI features (intelligent search, procurement agents, predictive maintenance). Build and integrate LLM-driven agents , RAG pipelines, and predictive models. Collaborate with software engineers to embed AI securely into production. Define AI standards , select tools, and shape the technical roadmap. Opportunity ...

Services industry. Experience with Generative AI, Large Language Models (LLMs), and advanced frameworks like: LangChain, LangGraph, Haystack LlamaIndex, CrewAI, AutoGen, Transformers (Hugging Face) and RAG pipelines. Proven experience in leading teams and managing complex AI projects. Expertise in machine learning, data modelling, and advanced statistical techniques. Strong programming skills ...

handling both single-step LLM tasks and complex multi-step agentic workflows. As AI Engineer you will: Architect and implement the LLM, agentic and RAG modules that power the platform's low-code environment. Build primitives, abstractions and orchestration components that allow clients to construct robust agentic workflows with deterministic ...

handling both single-step LLM tasks and complex multi-step agentic workflows. As AI Engineer you will: Architect and implement the LLM, agentic and RAG modules that power the platform’s low-code environment. Build primitives, abstractions and orchestration components that allow clients to construct robust agentic workflows with deterministic ...

across data pipelines, data lakes, data governance, AI model pipelines, and enterprise AI platforms. Apply knowledge of GenAI-specific data ingestion, vector store integration, RAG pipelines, agentic frameworks, and multi-modal data management. Collaborate on AI platform designs leveraging Dell’s product and partner ecosystem e.g. NVAIE, , , ClearML, OpenShift, etc. ...

across data pipelines, data lakes, data governance, AI model pipelines, and enterprise AI platforms. Apply knowledge of GenAI-specific data ingestion, vector store integration, RAG pipelines, agentic frameworks, and multi-modal data management. Collaborate on AI platform designs leveraging Dell’s product and partner ecosystem e.g. NVAIE, , , ClearML, OpenShift, etc. ...

across data pipelines, data lakes, data governance, AI model pipelines, and enterprise AI platforms. Apply knowledge of GenAI-specific data ingestion, vector store integration, RAG pipelines, agentic frameworks, and multi-modal data management. Collaborate on AI platform designs leveraging Dell’s product and partner ecosystem e.g. NVAIE, , , ClearML, OpenShift, etc. ...

across data pipelines, data lakes, data governance, AI model pipelines, and enterprise AI platforms. Apply knowledge of GenAI-specific data ingestion, vector store integration, RAG pipelines, agentic frameworks, and multi-modal data management. Collaborate on AI platform designs leveraging Dell’s product and partner ecosystem e.g. NVAIE, , , ClearML, OpenShift, etc. ...

autonomous AI What You'll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical ...

autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical ...

maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 3-6+ years building production backends/services , with shipped work ...

maintain CI/CD, monitoring/alerting, performance & error budgets, and safe rollback paths. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Essential Requirements 3-6+ years building production backends/services, with shipped work ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...

environment, including model registry, CI/CD for ML, feature stores, monitoring for drift, and retraining policies. Build reusable AI platform capabilities (e.g., orchestration, RAG services, prompt safety, connectors) that support multiple domains such as: Recruitment & Admissions: enquiry triage, lead scoring, document extraction, and decision support. Student Records & Registry: data … running Agile PoCs and MVPs and scaling them into production within complex organizations. Strong understanding of AI/ML techniques (e.g., classification, NLP, GenAI, RAG, prompt engineering), data pipelines, APIs, and microservices. Hands-on experience with cloud platforms (Azure preferred; AWS or GCP also valued) and MLOps tools (e.g., MLflow ...