76 to 100 of 217 Retrieval-Augmented Generation Jobs in the UK

develop GenAI applications for Code Translation, Text Extraction, Summarisation and SDLC Optimization etc. Hands-on Experience with AI Agents, Chat bots, RAG (Retrieval-Augmented Generation), and vector databases (PG vector/croma DB) Hands-on Experience with GenAI Performance Evaluation tools like Pegasus, Ragas ...

Knowledge of MLOps/LLMOps tooling (e.g., MLflow, Weights & Biases, Data Version Control) Experience developing LLM/GenAI applications, including prompt engineering and RAG architectures Ability to absorb complex scientific and technical concepts and apply them to operational solutions Strong communication skills for both technical and non-technical stakeholders Curiosity ...

clients Familiarity with RegTech, compliance, KYB/KYC, or financial services workflows Exposure to search-grounded generation, RAG architectures, or retrieval-augmented workflows Basic programming ability (Python or JavaScript); enough to read code, work with APIs, and write light scripting when needed, even … periphery; you'll be one of the people who shapes how it's configured, extended, and evolved. Real Problems, Real Impact: Search-grounded generation, multi-agent orchestration, structured output reliability, customer-specific workflow customisation; the work is complex, varied, and consequential. What you configure ships and gets used. ...

Weaviate, Chroma) Cloud (AWS/Azure/GCP) Docker, Kubernetes CI/CD, observability, testing for AI systems Experience Strong experience building LLM applications, RAG systems, or AI agents Understanding of prompt engineering, tool-calling, and orchestration frameworks Ability to work autonomously in a scale-up environment A product-focused ...

such as **AWS, Azure or Google Cloud** Data platforms including **Databricks, Snowflake or BigQuery** Python and modern ML frameworks Generative AI, LLM integration and RAG pipelines MLOps tooling and modern ML lifecycle management. The profile Experience designing enterprise AI or data platforms in complex technology environments. A background delivering large ...

Vercel AI. Core Engineering: Python (FastAPI) or TypeScript (Express) for microservices; React/Next.js for front-end requirements. The AI Layer: Practical application of RAG, intelligent agents, and LLMs (OpenAI, Anthropic, Mistral, Llama). Data & Infrastructure: Vector databases (ChromaDB, Pinecone, PGVector, Qdrant), SQL/NoSQL, and Docker/Kubernetes. Production ...

machine learning lifecycle, model deployment, and MLOps practices Desirable Skills Experience with frameworks such as TensorFlow, PyTorch, or Hugging Face Familiarity with prompt engineering, RAG architectures, and vector databases Knowledge of data governance and security standards within government environments Experience working in agile delivery teams What We Offer Competitive salary ...

requirements Strong background in AI, machine learning or data science Deep experience with LLMs and modern GenAI frameworks Experience designing AI architectures such as RAG and agent-based systems Strong Python and cloud experience across AWS, Azure or GCP Proven track record delivering AI solutions in a client facing environment ...

quality metrics: task success rate, groundedness, containment, FCR Experience on Google Cloud Platform (preferred) or equivalent hyperscale Deep understanding of LLMs, generative AI, RAG patterns, vector databases, embeddings, and prompt/guardrail engineering Desirable Skills/Knowledge/Experience Knowledge of MLOps/AgentOps, CI/CD, and observability Strong ...

sharing Mentor engineers and raise the overall architectural maturity of the organization GenAI, ML & AI Architecture Architect customer facing GenAI features using LLMs, including RAG pipelines, prompt engineering frameworks, and conversational AI interfaces Define patterns for ML model integration: serving endpoints, API contracts, versioning, and health monitoring Partner with Data ...

modern vector databases. An understanding of Site Reliability Engineering best practices and Platforms/DevOps best practices. Exposure to AI concepts such as LLMs, RAG, and agent‐based systems (or a strong desire to learn). Experience previously working with central government and an understanding of government ways of working ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

Proven experience as an AI or Machine Learning Engineer with end-to-end model ownership Strong expertise in NLP and LLMs (transformers, fine-tuning, RAG, agents) Experience translating research and experimentation into production systems Solid understanding of MLOps, including CI/CD, monitoring and model lifecycle management Hands-on experience ...

business logic * Experience in small, fast-moving teams (startup, scaleup, or consulting) * Strong communication and documentation skills Nice to have: * Experience with LLM workflows (RAG, LangChain, etc.) * Experience in logistics, trade, or compliance data * Experience supporting client-facing or pre-sales conversations To Apply: Please email ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

problem-solving mindset. Strong Python skills Experience building ML models using frameworks like PyTorch, TensorFlow, or scikit-learn Hands-on experience with LLMs (prompting, RAG, fine-tuning, agents) Understanding of MLOps principles and tools (e.g. MLflow, DVC, Weights & Biases) Experience deploying solutions in AWS or cloud environments Familiarity with Docker ...

leveraging Azure AI Foundry as the pro-code platform - including multi-agent orchestration, tool integration (MCP, A2A, OpenAPI), agent memory, and enterprise knowledge grounding (RAG)* Assess opportunities for Intelligent Automation based on technical feasibility and business value, to enable prioritisation of the backlog* Evaluate opportunities against alternative internal solution options ...

systems (LangChain/LangGraph or similar) into production Hands-on experience with AI agents, agentic workflows, and multi-step reasoning Experience designing and implementing RAG systems with real-world impact Familiarity with MCP or similar orchestration patterns (FastMCP/FastAPI) Understanding of LLM limitations and trade-offs (hallucination, latency, cost ...

based solutions that directly improve customer outcomes. Key Responsibilities • Own 0-1 ML product development from prototype to production • Build and scale LLM systems (RAG, agents, NLP) • Translate product needs into experiments, MVPs, and production features • Collaborate with Engineering and Product to ship solutions • Work with large-scale cloud datasets ...

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 ...