76 to 100 of 148 Retrieval-Augmented Generation Jobs in the UK excluding London

/ML frameworks (e.g. LangChain, OpenAI API, Hugging Face, TensorFlow, PyTorch) * Comfortable writing production-ready code - Python particularly valued * Understanding of LLMs, prompt engineering, RAG pipelines or similar AI architectures * Ability to communicate technical ideas clearly to non-technical stakeholders * A proactive mindset - someone who spots opportunities and takes initiative ...

Architect Professional* Databricks Certifications* CDMP Certification* Terraform, Bicep or Infrastructure-as-Code experience* dbt, Airflow or modern orchestration frameworks* Experience supporting AI, GenAI or RAG-based architectures* Experience within Financial Services, Healthcare, Life Sciences, Energy, Retail or Manufacturing Robert Half Ltd acts as an employment business for temporary positions ...

prioritise AI opportunities across the organisation Required ExperienceEssential Deep expertise in Python and relevant AI/ML frameworks and SDKs Proven experience building RAG pipelines that operate effectively in production environments Hands-on experience with model packaging, deployment, and production AI workflows Strong understanding of enterprise infrastructure concepts including VNets ...

Familiarity with AI-assisted development tools such as GitHub Copilot, Claude, and Cursor. Azure AI Engineer certification or equivalent practical experience. Understanding of MCP, RAG architectures, or vector databases. Benefits Industry-leading training and development opportunities. Competitive salary with progression potential. 23-days holiday allowance, increasing with length of service ...

with agents, tool calling or LLM orchestration. A background in lawtech/legaltech or other regulated domains. Programming in Python. Deeper experience with LLMs, RAG techniques and evaluation. Knowledge of Docker, containerisation and serverless technologies. AWS Certification is a plus. Education and Experience: Bachelor’s degree in Computer Science, Information ...

agentic AI architectures, with experience integrating AI securely and at scale Practical experience in enterprise AI governance, prompt engineering, and deployment approaches (including RAG, fine-tuning considerations, and risk mitigation) Experience leveraging AI-assisted development tools (e.g. GitHub Copilot, Claude Code) to materially improve engineering productivity and delivery outcomes ...

prototypes, products and production ready solutions Designing and implementing end to end AI solutions that integrate with enterprise systems Working with LLMs, prompt engineering, RAG patterns, embeddings and fine tuning Developing AI agents and agentic workflows using modern frameworks Working with vector databases, APIs and modern data platforms Supporting … collaborative, high performing environment Experience Required: Strong Python and SQL experience Applied AI engineering, ML engineering or software engineering background Experience with LLMs, RAG, embeddings, prompt engineering or fine tuning Exposure to LangChain, LangGraph, Agent Development Kit or similar agent frameworks Experience with vector databases such as Pinecone, Chroma ...

Azure AI, GenAI and Agents. Experience with digital assistant solutions enhanced by AI such as Microsoft Copilot. Demonstrable experience with Retrieval-Augmented Generation. Solid understanding of MLOps. Ability to engage confidently with clients and present technical solutions clearly. It would be great if you had: Experience ...

deployment and monitoring. Lead end to end delivery of AI and LLM based solutions across underwriting and insurance journeys Build and deploy agentic workflows, RAG pipelines, and LLM powered applications for internal and customer facing products Work across structured and unstructured data, collaborating closely with Machine Learning and GenAI specialists ...

business environment Deep expertise in Python and modern AI/ML frameworks and SDKs Strong experience building and deploying production-grade AI solutions, including RAG pipelines and LLM-based applications Hands-on experience with AI deployment, operationalisation, monitoring, and scaling Strong understanding of enterprise infrastructure concepts including VNets, Entra ...

software engineering best practices. Support end-to-end MLOps processes including deployment, monitoring and model validation. Explore and implement generative AI capabilities including LLMs, RAG pipelines, and intelligent workflow automation. Key Skills: Commercial experience in Machine Learning Engineering, Applied AI, or related software engineering roles. Strong programming skills in Python … pipelines, and scalable deployment practices. Understanding of MLOps concepts including experiment tracking, model monitoring, and reproducibility. Exposure to Generative AI technologies including LLMs, RAG, or prompt engineering is a plus. Strong communication skills and ability to work effectively within cross-functional agile teams. ...

real-world systems. What you'll work on: Integrating AI components (including LLMs) into production applications and workflows Designing and implementing RAG (Retrieval-Augmented Generation) systems Evaluating LLM behavior, identifying failure modes, and implementing mitigation strategies Testing and debugging non-deterministic AI systems …/CD, IaC, testing, containers) Practical experience with AI systems, including LLM behavior and architectures Evaluation techniques and failure mode analysis Experience with RAG and applied AI system design Strong understanding of AI limitations and appropriate use cases Excellent communication and stakeholder engagement skills Eligibility for UK SC-level security ...

working across: Microsoft Azure (including AI services/Foundry) .NET and modern backend engineering APIs, microservices and DevOps practices GenAI/LLM tooling (RAG, prompt engineering, embeddings, vector search) This is a role for someone who enjoys solving challenging problems and building robust, real-world systems in a fast-moving ...

Requirements AWS Bedrock Expert: Hands-on experience with Bedrock Agents, Knowledge Bases, and model lifecycle management. AI/ML Depth: Strong grasp of FMs, RAG, tool-use, and the failure modes of agentic workflows. Security & Compliance: Deep knowledge of NIST AI RMF, OWASP LLM Top 10, and UK/ ...

senior client stakeholders to shape and deliver solutions Build and deploy end-to-end AI products , not just prototypes Apply technologies such as LLMs, RAG systems, and agentic workflows to real business problems Translate technical outputs into clear commercial value and measurable impact Mentor and develop consultants, helping build capability ...

approach with proven experience or strong hands-on interest in Azure's AI stack, including Azure AI Foundry, Azure OpenAI, AI Search, agents and RAG patterns. Strong practical knowledge of databases, including SQL and NoSQL platforms, schema design, data access patterns, query performance and secure data handling. Ability to design … OpenAI, Azure AI Foundry, Azure AI Search, Azure Functions, Azure SQL, Cosmos DB, AKS, storage services and related platform components. Contribute to AI and RAG features, including document ingestion, metadata enrichment, retrieval workflows, grounding, reasoning flows, template generation and agent orchestration. Support Microsoft Fabric and data ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

multiple levels, from Engineers through to senior technical leadership. What you'll be doing Building AI-enabled platforms and intelligent workflows Working with LLMs, RAG pipelines and embedding models Developing scalable backend services and AI integrations Collaborating across engineering, architecture and business teams Supporting AI deployment, governance and operational rollout … Environment Python, SQL LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases, FastAPI, APIs AWS, Azure, GCP or Databricks CI/CD, MLOps and LLMOps Requirements Strong software or data engineering foundations Experience delivering AI or AI-enabled solutions Exposure to cloud and modern data platforms Financial ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

backend engineering, and cloud systems , designing and deploying intelligent features that directly impact users. Expect to: Design and implement LLM-based applications (RAG pipelines, agents, copilots) Build scalable backend services using Python and Java Integrate AI into existing cloud-native SaaS platforms Own the full lifecycle: experimentation prototyping production Optimise … software engineering, with strong experience in Python and/or Java Hands-on experience working with LLMs (prompt engineering, fine-tuning, embeddings, vector databases, RAG) Experience building and deploying applications in cloud environments (Azure preferred, AWS/GCP also considered) Strong understanding of APIs, microservices, and distributed systems Familiarity with ...

Hands-on experience with LangGraph, LangChain, LlamaIndex, or CrewAI building multi-step agentic systems RAG & knowledge systems: Practical experience designing and building retrieval-augmented generation pipelines AI evals and observability: Experience building evaluation frameworks and using tools such as LangSmith to monitor and constrain ...

application Development: LLM fine-tuning adapting pretrained LLMs for specific tasks using techniques likeparameterefficient fine-tuning (PEFT) (e.g., LoRA, QLoRA). Implementing Retrieval-Augmented Generationpipelines to enhance the knowledge and accuracy of LLMs. Utilizing vector databases for efficient storage andretrieval of embeddings generated by LLMs. rafting ...

with executive leadership on long-term growth strategy There’s a major focus on practical AI implementation, including: GenAI and automation AI-enabled operations RAG/knowledge systems workflow automation contact centre and admissions optimisation data and analytics architecture The environment includes: Salesforce CRM LMS/learning platforms cloud infrastructure ...

doing Designing AI and GenAI architecture strategies and roadmaps Building scalable AI/ML and Agentic AI platforms Working across LLMs, RAG, embeddings and semantic search Supporting AI deployment, governance and integration patterns Collaborating with engineering, data, DevOps and business teams Advising clients on AI platform capability and implementation strategy … Environment Python LLMs, prompt engineering, fine-tuning, RAG LangChain, LangGraph, Agent frameworks Vector databases and semantic search MLOps and LLMOps AWS, Azure, GCP, Databricks Containerisation, Kubernetes and GPU infrastructure Requirements Experience designing enterprise AI or ML architectures Strong understanding of cloud and modern data platforms Exposure to scalable AI deployment ...