76 to 100 of 155 Retrieval-Augmented Generation Jobs in England

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 ...

Languages: Go Databases: MongoDB Messaging & Orchestration: Temporal, Kafka APIs: REST, gRPC, protobuf Infrastructure: Kubernetes, GCP Observability: Grafana stack AI/ML: LLMs (OpenAI, Gemini), RAG systems Development Tools: Claude Code, AI-assisted development workflows What Success Looks Like In your first 6 months, you will have Owned and driven ...

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 ...

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 ...

integrating AI/ML features via APIs (e.g. OpenAI, Anthropic or similar) Exposure to data pipelines, inference workflows, or retrieval-based systems (RAG, vector DBs or similar) Cloud & DevOps: AWS, Azure, or GCP Docker, CI/CD pipelines (GitHub Actions, GitLab CI or similar) Experience deploying and maintaining ...

Role 2: Applied AI Engineer Strong backend engineering experience (Python preferred) Deep knowledge of CI/CD, Docker, Kubernetes, and Git workflows Experience building RAG pipelines and AI agent orchestration systems Minimum 5 years of experience 📩 Interested? Message me here or email mmatysik@trg-uk.com ...

ambiguity. What We’re Looking For Consulting or product-led background with direct client exposure Strong Python and hands-on delivery of GenAI systems (RAG, LLM apps, agent workflows) Able to write production-quality, maintainable code Familiar with prompt engineering, evaluation, and tools such as LangChain/LangGraph Clear motivation ...

end. Strong Python and SQL skills; experience working with large datasets in the cloud. Hands-on experience with LLM-based systems (e.g. RAG, agentic workflows, NLP). Strong product and systems thinking with a practical, impact-led mindset. Nice to Have AWS experience (e.g. SageMaker, Bedrock, Lambda, S3, Redshift). ...

architectures Design end-to-end architectures using Azure OpenAI, Azure AI Search, ML, data platforms, compute, networking, and identity Select and define architecture patterns (RAG, agentic workflows, streaming vs batch, hybrid approaches) Define non-functional requirements: security, scalability, performance, cost, availability Ensure alignment with Azure best practices, security standards, compliance ...

Understanding of DevSecOps, secrets management, enterprise controls, and compliance • Familiarity with Jira and Confluence • GenAI/LLM Fundamentals: Understanding of LLM concepts (prompting, embeddings, RAG, model limitations), and how to apply them safely in engineering workflows • GenAI Tooling & Integration: Experience using and integrating GenAI-assisted tools (e.g., code assistants, chat ...

success building AI applications in investment banking, asset management, or capital markets domains. Deep expertise in ML, NLP, LLMs, retrieval‐augmented generation (RAG), embeddings, and modern MLOps practices. Strong experience working with both structured financial datasets and unstructured data sources (e.g., filings, call transcripts ...

They’re Looking For Proven experience designing & deploying AI/ML solutions Expert knowledge of LLMs, AI frameworks & agent-based systems Experience in building RAG Systems Cloud expertise across AWS/Azure/GCP Ability to turn complex problems into scalable architectures Passion for AI innovation (this ...

platforms. Your Skills and Experience Hands on experience building and deploying machine learning models end to end. Experience with LLMs, NLP, conversational AI, RAG pipelines and model fine tuning. Understanding of production grade ML workflows and CI CD principles. Experience building chatbots or NLP driven chat engines used in live ...

Experience working with LLM APIs and prompt engineering Great to have Additional exposure to data platforms such as Databricks or ThoughtSpot Understanding of RAG pipelines and vector databases (Hands-on experience across several of the above systemsis beneficial) Commercial & Operational Experience operating in a SaaS environment with a strong understanding ...

consultative mindset, able to set architectural direction and foresee emerging trends. Advanced knowledge of AI and ML technologies, including generative AI, agentic architectures, RAG pipelines, LLM integration, and responsible AI governance. Strong multi-cloud expertise with GCP, AWS, and Azure, including serverless, event-driven architectures, IaC, and container orchestration. Proven ...

catalog, data marketplace, feature store, or developer portals (e.g., Kaggle Datasets, Unity Catalog, Collibra, Alation, Atlan). Understanding of agentic AI patterns — tool-use, RAG, function calling — and how marketplace APIs can be exposed to LLM-based agents. Experience with search relevance & recommendation systems — ranking algorithms, semantic search, personalisation. Hands ...

delivering solutions at pace. Key Responsibilities Design and build scalable full stack applications Develop and deploy AI-powered solutions including LLM integrations and RAG systems Work closely with stakeholders to translate business needs into technical solutions Lead rapid prototyping and production delivery Contribute to architecture decisions across multiple projects Mentor ...

delivering solutions at pace. Key Responsibilities Design and build scalable full stack applications Develop and deploy AI-powered solutions including LLM integrations and RAG systems Work closely with stakeholders to translate business needs into technical solutions Lead rapid prototyping and production delivery Contribute to architecture decisions across multiple projects Mentor ...

production systems in a startup or fast-paced environment Familiarity with cloud platforms such as AWS, GCP, or Azure Exposure to AI, LLMs, or RAG is beneficial You’ll join a small, product-focused team with high ownership and the opportunity to influence both engineering direction and product development. ...

fast-moving environment. Skills and experience Strong experience with Python Experience building production systems using LLMs or generative AI Knowledge of prompt engineering, RAG, or agent-based architectures Experience working with APIs, distributed systems, or event-driven architectures Understanding of authentication and security protocols Experience building secure, scalable platforms Knowledge ...

automation Hands-on exposure to tools such as Azure OpenAI, Copilot Studio, or similar platforms Understanding of generative AI approaches, including prompt engineering and RAG Experience integrating enterprise systems via APIs and connectors Familiarity with Power Platform (Power Automate, Power Apps) Awareness of governance, security, and data protection considerations Strong ...

Understanding and hands-on experience with Transformer models and LLMs (e.g., GPT, LLaMA, Mistral, Claude). Skills in fine-tuning, prompt engineering, and building RAG pipelines. Familiarity with Agent Frameworks (LangChain, LlamaIndex, CrewAI, AutoGen). Knowledge of reinforcement learning methods or tools (Q-learning, policy gradients, RLlib). Why Join ...

operating as a senior data leader in a commercially driven environment, with a clear track record of delivery. Key requirements include: Experience using Langchain, RAG systems and Vector databases Proven ability to lead analytics and insight functions, with a bias towards hands on delivery rather than large scale people management. ...

unified decisions. Own operability: CI/CD, monitoring/alerting, performance optimisation, safe rollbacks. Build and integrate AI features (LLM/agent workflows, RAG/vector DB integration, eval hooks, cost/safety guardrails). Help evolve our reusable product templates and shared architecture across the portfolio. Essential Requirements ...

work. You use them to build faster and think better, and you're comfortable designing AI-powered applications: agentic workflows, LLM integrations and RAG applications. You learn fast. You can walk into patent law or drug discovery and within days be designing useful things. You communicate clearly. ...