26 to 50 of 98 Retrieval-Augmented Generation Jobs in London

features-from data ingestion and feature engineering to model training, evaluation, and deployment Develop LLM/GenAI solutions (prompt engineering, tool use, guardrails) and RAG pipelines (chunking, embeddings, vector search, caching, re ranking) Optimise training and inference performance via batching, quantisation, distillation, LoRA/PEFT, accelerator utilisation (GPU/… building GenAI applications using Python Hands-on Experience with API Development and Microservices architecture and End to End integrations Knowledge of RAG (Retrieval-Augmented Generation) and ADK, MCP Solid understanding of LLMs, prompt engineering, and graph-based workflows Hands-on Experience with API Development ...

About the Role We are seeking a Senior Full-Stack Engineer to join our team and help build the next generation of AI solutions. This is a unique opportunity to be a key technical contributor in a fast-paced, innovative environment where you'll wear many hats … trade-offs Experience with prompt engineering techniques — few-shot prompting, chain-of-thought, structured output, and instruction tuning Practical knowledge of RAG (Retrieval-Augmented Generation) architectures, including chunking strategies, vector embeddings, and similarity search Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex ...

deploy Generative AI solutions using large language models (LLMs) Develop and optimise prompt engineering strategies for production use cases Build end-to-end pipelines (RAG, embeddings, vector search) for scalable GenAI applications Work with both structured and unstructured datasets, including text-heavy data Collaborate closely with data engineering and software ...

hands on experience in deploying and hosting Large Foundational Models. Experience with LLM architecture (e.g., Transformer, GANs, VAEs), Fine-tuning, Retrieval Augmented Generation techniques and Context embedding, Vector database technologies and Semantic Search techniques & tools. Experience in writing robust and efficient code in Python. ...

deploying models in production and building scalable AI software. Deep understanding of advanced modelling methods: deep learning, GNNs, computer vision, sensor fusion, vector embeddings, RAG, generative AI . Experience designing AI/ML architectures and providing strategic technical guidance to enterprise clients. Proficiency with cloud compute platforms for training … impact. Apply advanced AI techniques—including deep learning, classical ML, graph neural networks, object detection, sensor fusion, vector embeddings, retrieval‐augmented generation (RAG), and generative AI models —to design systems that learn, adapt, and operate autonomously. Provide strategic architectural guidance on AI systems, ensuring ...

healthtech Deployed LLMs or predictive models in production environments Familiarity with FHIR, HL7, or secure health data pipelines Hands-on LLM work: prompt engineering, RAG, fine-tuning (LoRA/QLoRA) The Package Competitive salary + meaningful equity Work directly with and be mentored by two highly experienced co-founders Spacious ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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). ...