376 to 400 of 960 Retrieval-Augmented Generation Jobs in England

learning or Generative AI to be a genuine peer to your architects and engineers. You should be able to explain, without notes, how a RAG architecture fails in production and what you would do about it. • Team leadership. Experience leading and coaching consultants, engineers or architects within your programme ...

architecture for unstructured document ingestion, reasoning, and output generation Design LLM-centric and hybrid AI architectures combining: OCR RAG systems Agentic workflows Design and govern prompt strategies and prompt frameworks for: Loss run and insurance document extraction & normalization Claims summarization, triage, and fraud signal generation Underwriting … platforms Experience integrating AI solutions into enterprise systems .NET (Backend) and React (Frontend) Developer Prompt Engineering & LLM Design Retrieval Augmented Generation (RAG) Architectures Skills and Personal attributes we would like to have: OCR systems for document ingestion and classification AI Governance & Token Economics ...

prompt engineering combined with traditional software/automation engineering. Hands-on expertise utilizing GitHub Copilot, Microsoft Copilot, and Claude Code for code generation, review, and refactoring. Deep proficiency in PowerShell scripting, enterprise automation, and system administration. Strong database background, specifically with SQL Server. Programming proficiency in at least … with Azure AI, OpenAI APIs, or Anthropic APIs. Solid understanding of CI/CD, DevOps, and enterprise automation platforms. Familiarity with RAG (Retrieval-Augmented Generation) architectures, vector databases, and knowledge systems. Previous experience deploying AI solutions within large-scale or regulated enterprise environments. ...

libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps and production AI practices, including CI/CD, model versioning ...

libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps and production AI practices, including CI/CD, model versioning ...

libraries such as PyTorch, TensorFlow, scikit-learn, XGBoost or equivalent. Practical knowledge of Generative AI techniques, including large language models, prompt engineering, retrieval-augmented generation, model evaluation and responsible AI considerations. Understanding of MLOps and production AI practices, including CI/CD, model versioning ...

systems Driving adoption of Agentic AI across multiple engineering teams Defining reusable engineering standards, architectures and implementation patterns Designing and evolving production RAG and retrieval architectures Establishing effective LangGraph/LangChain patterns for agentic applications Improving AI evaluation, testing, observability and production monitoring Developing guardrails, controls and approaches … Python and strong software engineering fundamentals Production Machine Learning/AI engineering AWS Agentic AI and LLM-based applications Retrieval Augmented Generation (RAG) LangChain and/or LangGraph Vector databases and retrieval MLOps and production deployment AI evaluation, testing and observability ...

Company Description At IFS, we're building the next generation of AI-native enterprise software, transforming how some of the world's largest organisations manage assets, operations and critical services. This is an opportunity to work at the forefront of modern AI engineering, building intelligent products that combine … orchestration capabilities. Experience evaluating AI solutions for quality, reliability, latency, cost and security. Understanding of prompt engineering, context engineering, Retrieval‐Augmented Generation (RAG), tool calling and modern AI application architectures. Experience applying AI‐assisted engineering practices throughout the software development lifecycle, including specification, implementation ...

scientific/clinical domain. Nice to Have (Desirable Requirements) ML - Experience of classical ML and NLP methods Software craft – testing, observability, documentation, code review RAG pipelines at depth - experience building secure, compliant ingestion and retrieval systems with provenance tracking, including web automation, parsing, and document processing Agent frameworks ...

SQL. Solid experience working on data science or AI projects. Experience with AWS, Azure or GCP Experience with ML and modern LLM/RAG systems. Strong communication skills and ability to collaborate with international teams Experience delivering data-driven solutions in a production environment For Immediate interview consideration please contact ...

automated testing with pytest • Experience supporting and improving large, established production codebases, with a strong understanding of secure software development Experience with LangChain, RAG, vector databases, Azure, Entra ID, OAuth/OIDC, Redis, Terraform, Helm, Prometheus, Grafana or OpenTelemetry would be valuable but is not essential. Current SC clearance ...

including AI and ML services Solid foundation in software engineering principles for scalable, production-grade systems Experience designing and delivering enterprise AI solutions including RAG-based architectures using vector databases Proven experience delivering full-stack AI or ML systems within enterprise environments Strong understanding of advanced agent architectures, reasoning systems ...

programming languages such as Python, Java, TypeScript, and Go, and have experience with REST APIs and databases.You understand generative AI methodologies such as RAG, fine-tuning, agents, reasoning workflows, and more.You plan architecture and software with foresight to ensure automation, stability, and maintainability.Knowledge of cloud technologies (Azure or AWS), data ...

management SQL and data integration patterns (batch/stream), data quality controls LLM/agent patterns: prompt/version management, tool/function calling, RAG, safety guardrails Observability and production support (logging/metrics/tracing, incident triage) Opening up a world of opportunity. Being open to different points ...

technical teams or workstreams. Strong foundations in machine learning and statistics, with demonstrable production experience. Practical experience with LLMs and generative AI — prompting, RAG, fine-tuning, and evaluation. Exposure to AI agents and orchestration frameworks, and an appetite to deepen it. Proficiency in Python and the modern data/ ...

+ security considerations). Be the AI champion/evangelist within the tech team. You will set reference architectures for predictive ML, generative AI, RAG, and agentic systems; choose platforms and patterns teams can reuse; and hold the line on quality, governance, and operational readiness. The role requires strategic thinking ...

support controls and audits. Architecture across cloud + AI: Strong software architecture background spanning cloud‐native, integration, and GenAI/agentic patterns (e.g., RAG, orchestration, tool use), with a pragmatic approach to delivery. GenAI across the SDLC: Practical use of GenAI in delivery, including requirements elaboration, ADRs (Architectural Decision Record ...

LangGraph, including agent design, state/memory management, and graph-based orchestration.Proficiency in ML/NLP and generative models; experience with embeddings, vector stores, RAG, and LLM integration/fine-tuning (OpenAI, LLaMA, Cohere, etc.)Strong coding in Python and experience with frameworks/tools such as FastAPI, PyTorch/ ...

detection that balance performance, explainability, and operational risk. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence ...

detection that balance performance, explainability, and operational risk. Lead a team of Data Scientists Drive innovation in LLM-based security automation, including prompt engineering, RAG pipelines, and fine-tuning of models. Collaborate with Data Engineers to build scalable, secure, and production-ready ML pipelines on GCP. Partner with cyber defence ...

Redis, and REST API design and integrationExperience with test-driven development and CI/CD workflowsExperience integrating AI technologies such as OpenAI, LangChain, or RAG pipelinesUnderstanding of authentication and authorization approaches including OpenID and SAMLExperience deploying cloud-native services on AWS with containerized infrastructure and KubernetesExperience with message queuing systems ...

judgement around data access, PII handling, RBAC, and the security implications of letting AI systems act autonomously. Preferred experience Familiarity with agentic patterns (MCP, RAG, evaluation frameworks) and the maturity to know when a simpler approach beats a more sophisticated one. Experience in a scale-up, SaaS, or other fast ...

data processing) and cloud computing platforms (AWS, Azure, or GCP). Hands-on experience with a range of AI concepts, such as LLMs, VLMs, RAG, Agents, MCP, computer vision, GraphML, recommendation systems or reinforcement learning. Familiarity with machine learning frameworks (PyTorch, TensorFlow etc). Strong proficiency in software development (Python ...

using frameworks such as LangGraph and related orchestration tools. Apply modern techniques in:NLPGenerative AIEmbeddings and semantic representationsRetrieval-augmented generation (RAG)AI reasoning and workflow orchestration Evaluate emerging AI models, tools, and frameworks and contribute recommendations for experimentation and adoption. Contribute to prompt engineering, grounding strategies … context management, and hallucination mitigation efforts. Support integration of scientific metadata, ontologies, and knowledge assets into AI-powered workflows. Search, Retrieval & RAG Systems Design, develop, and optimize search and retrieval pipelines , including lexical, vector, and hybrid retrieval approaches. Contribute to the development and enhancement ...

understand what makes agents perform well in production and how to systematically improve quality through observability and evaluation. Experience with voice AI platforms, RAG systems, and LLM orchestration frameworks is highly desirable. You bring exceptional communication skills, customer empathy, and the drive to build AI solutions that transform enterprise operations … integration Proficiency in Python (production-grade, async programming, type hints) and JavaScript/TypeScript (full-stack development) RAG implementation experience built retrieval-augmented generation systems with vector databases MLOps & deployment hands-on with Docker, Kubernetes, CI/CD pipelines, and infrastructure-as-code Cloud ...