1 to 25 of 26 Remote/Hybrid Retrieval-Augmented Generation Jobs in London

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, research).* Familiarity with front ...

just proofs of concept. AI Engineer Responsibilities Own and evolve a production-ready AI platform Design and deliver AI-powered client solutions, including RAG pipelines, agent architectures, LLM integrations, and protocol-driven tooling Build scalable AI systems using modern software engineering best practice (clean code, testing, CI/CD) Develop … experience with Large Language Models (LLMs), including API integrations, prompt engineering, agent workflows, and Retrieval-Augmented Generation (RAG) systems Experience shipping AI-powered features into production environments Proficiency in at least two modern programming languages Familiarity with emerging AI tooling (e.g. MCP, A2A, Threads ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. ...

platform capabilities in a B2B SaaS environment, with a track record of shipping at enterprise scale. Deep understanding of agentic AI patterns, LLM orchestration, RAG, fine-tuning, and evaluation frameworks — and the ability to translate these into compelling, reliable product experiences. Experience managing the full ML lifecycle — development, training, evaluation ...

through cutting edge technology, AI and data driven solutions. This is an exciting opportunity to join a growing team focused on building next generation GenAI applications and scalable full stack solutions, working on complex client engagements and shaping enterprise level AI solutions. The Role As a Senior GenAI … LangChain, LangGraph or CrewAI * Experience working with Large Language Models such as OpenAI, Anthropic, Llama or Mistral * Experience with Retrieval Augmented Generation, AI agents or similar GenAI architectures * Experience working with AWS or Azure in production environments * Docker and containerisation experience, Kubernetes desirable * Experience ...

through cutting edge technology, AI and data driven solutions. This is an exciting opportunity to join a growing team focused on building next generation GenAI applications and scalable full stack solutions. The Role As a GenAI Full Stack Engineer, you will play a key role in designing, building … LangChain, LangGraph or CrewAI Experience working with Large Language Models such as OpenAI, Anthropic, Llama or Mistral Exposure to Retrieval Augmented Generation or AI agents is beneficial Experience with AWS or Azure Docker and containerisation experience, Kubernetes beneficial Experience with CI CD pipelines using ...

deployment. Build MVPs and collaborate closely with Engineering and Product teams to bring ideas to life. Apply advanced machine learning and LLM techniques, including RAG, agents and NLP, where they drive meaningful customer and product impact. Work with large-scale datasets in cloud environments to deliver robust, scalable systems. Contribute … end. Proficiency in Python and SQL, with experience working on cloud-scale datasets. Practical experience with LLMs and applied AI, such as RAG, agents, or NLP. Experience building customer-facing or product-centric ML systems. A product-focused mindset, balancing experimentation with real-world value. Useful additional experience includes ...

equity & benefits Reports to: CTO Overview of the Role This is a rare opportunity to shape and lead the evolution of a next-generation, AI-native data platform within a high-growth, well-funded scale-up. As Director of Data Engineering, you will operate at the intersection … development of a knowledge graph and data enrichment pipelines to unlock proprietary data value Drive AI/ML Ops maturity , including LLM deployment, RAG pipelines, and evaluation frameworks Engineering & Architecture Define scalable, cloud-native architectures across GCP (preferred) and AWS Lead cross-cloud data orchestration , ensuring seamless data flow from ...

Experience Strong commercial experience using Python in applied AI or machine learning contexts Proven delivery of end-to-end AI solutions such as RAG systems, LLM-powered applications, or agent-based workflows Experience working closely with clients or end users, translating real requirements into technical solutions Solid software engineering fundamentals … including maintainable code, testing, and collaboration with engineering teams Practical knowledge of prompt engineering, RAG pipelines, agent workflows, and model evaluation Familiarity with modern GenAI tooling such as LangChain, LangGraph, or similar frameworks Motivation to work in a startup environment, with comfort around ambiguity, pace, and ownership What They Offer ...