126 to 150 of 419 Retrieval-Augmented Generation Jobs in the UK

evolve and pragmatic decision-making is essential. Nice to Have Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures. Experience working with containerised applications and modern CI/CD pipelines. AWS certifications, including Solutions Architect or Machine Learning Specialty. ...

evolve and pragmatic decision-making is essential. Nice to Have Familiarity with vector databases and Retrieval-Augmented Generation (RAG) architectures. Experience working with containerised applications and modern CI/CD pipelines. AWS certifications, including Solutions Architect or Machine Learning Specialty. ...

clear, evidence‐based recommendations on viability, effort and expected value. Build and maintain a living library of production‐ready patterns covering areas such as RAG, agentic AI, LLM integration, prompt engineering and responsible AI guardrails. Facilitate cross‐domain engineering forums, design reviews, and communities of practice, creating the conditions … agentic systems. Proficiency in Python and modern cloud‐native development practices (Azure preferred). Practical experience with prompt engineering, retrieval‐augmented generation (RAG), fine‐tuning, and AI orchestration frameworks such as LangChain, Semantic Kernel, or equivalent. Strong understanding of data engineering fundamentals ...

/Specialist Expertise Experience building and deploying GenAI solutions using frameworks such as LangChain and Azure OpenAI. Development of enterprise-grade RAG (Retrieval-Augmented Generation) systems, including context engineering and multimodal data pipelines. Design and deployment of autonomous multi-agent systems using modern orchestration ...

delivered across the organization. Key Responsibilities: Lead the design and build of end-to-end generative AI applications using LangGraph, including agentic workflows, RAG pipelines, and LLM-integrated services. Define and drive architecture for scalable, secure, reliable, and maintainable AI solutions deployed in production. Develop robust, well-tested backend APIs ...

equivalents. Ability to apply AI agents across all phases of delivery: requirements, architecture, coding, testing, documentation, and deployment. Experience with LLMs, prompt engineering, RAG, vector databases, and orchestration patterns. Strong Azure expertise preferred, with hands‐on exposure to AWS and GCP. Experience integrating AI into distributed systems, microservices, APIs ...

across the others: Agentic AI & LLM Engineering: Design and build production-grade agentic systems using major LLM SDKs and agent frameworks; deep knowledge of RAG, MCP servers and prompt engineering at scale. Strong opinions on secure, resilient enterprise deployment of LLM-powered systems; current knowledge of the latest model capabilities ...

from idea to production. Strong knowledge of deep learning, NLP, and LLM architectures and experience in fine-tuning LLMs or training domain-specific models, RAG, embeddings, and vector databases. Familiarity with agentic AI concepts and orchestration frameworks. Experience deploying AI solutions on cloud platforms such as Azure, AWS, or GCP. ...

data platforms (Snowflake, Databricks, BigQuery, Azure Synapse) and real-time streaming (Kafka, Flink, Spark Streaming). Familiarity with generative AI patterns (LLM fine-tuning, RAG, prompt engineering, function calling, agent frameworks) and graph/knowledge graph technologies. DevOps/SRE practices at scale: CI/CD, IaC (Terraform, Pulumi), observability ...

providers AI coding tools: GitHub Copilot, Cursor or equivalents Prompt engineering and context design patterns Retrieval-augmented generation (RAG), vector search, embeddings (desirable) Evaluation frameworks and automated quality pipelines for AI outputs Technical and Professional Expertise Solid production experience with C# .NET, including working ...

providers AI coding tools: GitHub Copilot, Cursor or equivalents Prompt engineering and context design patterns Retrieval-augmented generation (RAG), vector search, embeddings (desirable) Evaluation frameworks and automated quality pipelines for AI outputs Technical and Professional Expertise Solid production experience with C# .NET, including working ...

deploy AI‐powered applications including chatbots, copilots, document intelligence, and decision‐support 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 … experience with ChatGPT/OpenAI APIs and/or Anthropic Claude.* Practical experience with prompt engineering, system prompts, and evaluation techniques.* Experience implementing RAG architectures, vector databases, and semantic search.**Cloud & Platforms*** Strong experience with Google Cloud Platform (GCP), ideally Vertex AI.* Experience deploying services using Docker/containerised workloads. ...

doesn't make sense, and delivering in short, time‐boxed cycles where validated outcomes matter more than perfect solutions. Agentic AI/LLMs: Building RAG pipelines, tool‐augmented agents and agentic workflows; familiar with prompt engineering, context management, evaluation and observability. Agent fundamentals: Understands how agents use memory ...

implementing end-to-end AI/ML solutions from development through to deployment and monitoring * Building scalable Generative AI and Agentic AI solutions, including RAG pipelines and orchestration frameworks * Implementing MLOps frameworks, governance and best practices * Translating business requirements into cloud-native AI and data architectures across Azure ...

cloud‐native delivery patterns What Will Make You Stand Out Experience building, deploying, and evaluating multi‐agent AI solutions, agent orchestration patterns, RAG architectures, and enterprise integrations Knowledge of agent communication protocols and interoperability patterns, such as MCP and A2A Ability to balance innovation with practical delivery considerations, including risk ...

from idea to production. Strong knowledge of deep learning, NLP, and LLM architectures and experience in fine‐tuning LLMs or training domain‐specific models, RAG, embeddings, and vector databases. Familiarity with agentic AI concepts and orchestration frameworks. Experience deploying AI solutions on cloud platforms such as Azure, AWS, or GCP. ...

experience with cloud platforms (AWS, Azure, GCP) Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and Gen AI/Agentic frameworks (LangGraph, LangChain, VectorDBs, RAG) Understanding of responsible AI practices, including guardrails, hallucination mitigation, and output quality management for AI systems Experience designing and scaling low-latency, customer-facing ...

founders with a track record of successful exits. With teams across the US, UK, and India, we’re building the next generation of enterprise AI and we’re looking for exceptional people to help us scale. Who You Are You're a customer‐focused AI consultant and software … designing and delivering enterprise software, cloud, or AI solutions for customers. Strong understanding of Large Language Models, Generative AI, Retrieval‐Augmented Generation (RAG), AI agents, and enterprise AI architectures. Experience with cloud platforms including AWS, Azure, or Google Cloud Platform. Understanding of APIs, microservices ...

such as LangChain, LangGraph, LlamaIndex, and Hugging Face, including agentic and multi‐step workflows. Proven experience designing and implementing Retrieval‐Augmented Generation (RAG) systems, including vector databases (pgvector, Pinecone, Weaviate, FAISS, etc.) and advanced retrieval strategies (hybrid search, re‐ranking, metadata filtering ...

advanced degree preferred.* **Agentic AI/LLMs:** LLM orchestration, prompt engineering, context-window management and production guardrails; MCP, tool-augmented agents and RAG at enterprise scale.* **Enterprise integration:** Integrating AI into enterprise systems via APIs and data pipelines, with strong security, data governance and residency practices.* **Cloud ...

border data residency and audit requirements). AI Foundations: Experience designing agentic or AI‐enabled solutions, including patterns such as retrieval‐augmented generation, tool and API integration, evaluation and human‐in‐the‐loop design, would be a significant advantage, though it is not essential. ...

Azure and AWS, with experience supporting and maintaining cloud‐based infrastructure. Hands‐on experience working with LLM‐based solutions, including Retrieval‐Augmented Generation techniques and prompt engineering approaches. Hands‐on experience working with data processing frameworks and platforms, including batch and streaming pipelines. Practical ...

MLflow, Weights & Biases or Data Version Control Experience designing, testing and evaluating machine learning experiments Knowledge of Generative AI, LLMs, prompt engineering and RAG architectures Experience with LLMOps tools such as LangChain, LangSmith or LangGraph Strong understanding of model monitoring, validation and governance Excellent communication and stakeholder management skills Ability ...

/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support ...

Experience working on recommendation systems, LLM applications or other AI/ML systems Practical experience with Kubernetes, EKS, Docker, MLOps Prior exposure to LLMs, RAG, Knowledge Graph Technologies, OpenSearch and vector databases Prior experience collaborating with data scientists #J-18808-Ljbffr ...