126 to 150 of 242 Retrieval-Augmented Generation Jobs in England

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

tier enterprise clients. Partner directly with technical and business teams to identify pain points and architect high-impact AI-driven solutions. Build and deploy production-grade systems leveraging LLMs, RAG pipelines, multi-component AI programs (MCPs), and agent frameworks . Conduct applied research to solve novel, previously unseen problems — bringing cutting-edge innovation from concept to production. Contribute to internal More ❯

decisions. Own operability: set up and 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 and clear ownership. Strong Python More ❯

decisions. Own operability: set up and 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 and clear ownership. Strong Python More ❯

optimising prompts to ensure consistent, high-quality LLM outputs. Building and deploying AI-powered workflows that connect LLMs with business applications, APIs, and automation tools. Implementing RAG (Retrieval-Augmented Generation) pipelines to integrate enterprise data. Rapidly prototyping and iterating on AI solutions to demonstrate business value. Advising on responsible AI practices, governance, and compliance. … frameworks. Proficiency in Python or JavaScript for prototyping and integration work. Experience using automation platforms (UiPath, Power Automate, Zapier, n8n) and APIs. Knowledge of vector databases and embeddings for RAG pipelines. Excellent communication skills and the ability to translate business needs into technical solutions. This is a fantastic opportunity for an engineer who loves solving problems with AI and wants More ❯

SINDy) Working knowledge of cell biology. Experience with Python, C, R or related scientific computing languages. Preferred Qualifications - Experience working with causal representation learning Experience with RAG (retrieval-augmented generation) and GraphRAG a big plus Experience with building and deploying software on GitHub, PyPI, Anaconda Cloud, and Docker Hub, as well as use of More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They're More ❯

public and private sector clients. Details- Proven experience building Agentic AI systems (e.g. autonomous agents, multi-agent orchestration, tool use, memory, planning) Strong hands-on skills in Python, LLMs, RAG, and LangChain or similar frameworks Deep understanding of GCP services: Vertex AI, BigQuery, Cloud Functions, Pub/Sub, etc. Experience with batch and real-time data pipelines Familiarity with AI More ❯

to public and private sector clients.Details- Proven experience building Agentic AI systems (e.g. autonomous agents, multi-agent orchestration, tool use, memory, planning) Strong hands-on skills in Python, LLMs, RAG, and LangChain or similar frameworks Deep understanding of GCP services: Vertex AI, BigQuery, Cloud Functions, Pub/Sub, etc. Experience with batch and real-time data pipelines Familiarity with AI More ❯

be necessary for meetings, but 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 to production. Lead development across More ❯

categories AI You will have: Actively used AI in your workflows Deployed AI systems at scale Built real-world systems with LLMs Used agent frameworks, like LangGraph or LangChain, RAG pipelines It would be interesting if you have: Contributed to the AI/ML community (open-source, publications, talks) Experienced optimising AI models through fine-tuning, distillation, or human-in More ❯

categories AI You will have: Actively used AI in your workflows Deployed AI systems at scale Built real-world systems with LLMs Used agent frameworks, like LangGraph or LangChain, RAG pipelines It would be interesting if you have: Contributed to the AI/ML community (open-source, publications, talks) Experienced optimising AI models through fine-tuning, distillation, or human-in More ❯

team that’s pushing boundaries in 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 solutions What You’ll More ❯

team that’s pushing boundaries in 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 solutions What You’ll More ❯

JavaScript, C#) Proven experience building solutions with the Microsoft Power Platform Familiarity with Azure or AWS A strong interest in AI/LLMs. Practical experience with Dify, or building RAG applications is a major plus. More ❯

JavaScript, C#) Proven experience building solutions with the Microsoft Power Platform Familiarity with Azure or AWS A strong interest in AI/LLMs. Practical experience with Dify, or building RAG applications is a major plus. More ❯

a founder or founding engineer - you know what it means to balance craft, ownership, and speed Familiarity with tools that power today's AI agents: eval frameworks, agent tooling, RAG pipelines, and prompt engineering Prior experience with React, TypeScript, and/or Go Previous roles where you interfaced with customers or led technical projects with external stakeholders Our values Trust More ❯

Partnering with stakeholders to scope problems and identify the right solution - whether leveraging existing AI tools or building custom workflows & solutions. Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack. Building and maintaining production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing … of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring. Proven experience deploying and operating ML systems in production (batch and real-time). Familiarity with RAG architectures, prompt engineering, and evaluation techniques. Strong grasp of security, privacy, and governance principles (IAM, secrets, PII handling). Excellent communication skills and ability to work with non-technical stakeholders. More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯

AI capabilities at scale. Automating infrastructure/model deployments using Kubernetes, Terraform, and CI/CD pipelines. Building and managing vector databases and retrieval systems to support RAG pipelines. Working with LLM frameworks like LangChain , LangGraph , CrewAI , or similar tools. Partnering closely with engineering, security, and product teams to deliver safe, production-ready AI systems. What They’re More ❯