13 of 13 Permanent Retrieval-Augmented Generation Jobs in Central London

varied use cases. Build agentic workflows and reasoning pipelines using frameworks such as LangChain, LangGraph, CrewAI, Autogen, and LangFlow. Implement retrieval-augmented generation (RAG) pipelines using vector databases like Pinecone, FAISS, Chroma, or PostgreSQL. Fine-tune prompts to optimise performance, reliability, and alignment. Design and implement memory modules for short-term and long-term … cloud AI tools, observability platforms, and performance optimisation. This is an opportunity to work at the forefront of AI innovation, where your work will directly shape how next-generation systems interact, reason, and assist. More ❯

driven architectures tailored to finance-specific use cases, including: Intelligent document processing (KYC/AML) Conversational finance assistants (customer service, advisory) Automated risk and compliance workflows Synthetic data generation for testing and modeling Personalized financial insights and reporting Select the right GenAI technologies (open-source, proprietary, cloud, or hybrid) to meet client goals without being tied to a … stakeholders in both business and technology. Experience working in or with regulated financial environments and navigating security and compliance requirements. Preferred Qualifications Experience with retrieval augmented generation (RAG), fine-tuning, model evaluation, or deploying models in production environments. Understanding of financial risk modeling, model validation, or operational risk frameworks. Knowledge of regulatory frameworks such More ❯

and be able to make sense of ambiguous problem statements. Our client problems can range in a variety of domains. Some examples, but not limited to, are building a RAG leveraging open source or commercial LLMs, building a Multiagent system, leveraging Small Language models that could be locally resident, problems in computer vision, speech, Natural Language Processing (NLP), multilingual models More ❯

tools for modelling. Working experience on Modern data platforms which involves Cloud & related technologies Keeps track of industry trends and incorporates new concepts Advanced understanding of AI concepts , LLMS, RAG, Agentic apps Advance Understanding in Data visualization and Data pipelines. Excellent understanding of traditional and distributed computing paradigm. Experience in designing and building scalable data pipelines. Should have excellent knowledge More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

work on fine-tuning, integrating, and scaling LLMs for real-world applications. Key Skills: Strong Python engineering background Experience with LLMs (e.g. Hugging Face, OpenAI, LangChain) Model fine-tuning, RAG pipelines, vector databases (e.g. FAISS, Pinecone) Cloud (AWS/GCP), CI/CD, Docker Bonus: Knowledge of model optimization, quantization, or open-source contributions. 📩 If interested send your CV to More ❯

What We’re Looking For Strong experience in software engineering (preferably 5+ years) Proficiency with Python, data processing, microservices, and distributed systems Experience working with Large Language Models (LLMs), RAG pipelines, or similar AI tooling Comfortable owning technical delivery and leading small teams Strong communication skills - able to engage with both technical and non-technical stakeholders Ability to manage multiple More ❯

Functionally :Workside-by-sidewitharchitects,backendengineers,telcoexperts,andproductmanagers. Candidatesshouldhaveexperienceinsomeofthefollowingareas: Stronghands-onexperienceintegratingAI/MLAPIsintoproductionsystems StrongPythonskills+comfortwithdatasciencelibraries(Pandas,NumPy,etc.) Experiencewith voice-relatedAI (STT,TTS,orVoiceAI) Deepunderstandingof latencyvsaccuracyvscost trade-offs Familiarwith promptengineering and RAG techniques Backgroundinmicroservices,APIs,andcloud-baseddeployments ExperiencewithMLOpstoolsandmonitoringplatforms(e.g.,DataDog,Arize) Anyexperienceinthefollowingareasisanicetohave: Experiencewith VoiceLMMs (likeGPT-4o,NovaSonic) Previousworkin telecommunications orreal-timespeechsystems Familiaritywith voicecloning , multilingualmodels ,or PEFT techniques Knowledgeofprivacyandsecurityprotocolsforvoicedata MastersdegreeinML,AI,orarelatedfield Thisisacareerdefiningroleandanopportunitytocreatesomethingtrulyspecial.ItisbasedinLondonwithofficesnearBarbicanand3daysaweekon More ❯

and wants to shape how AI is used in the physical world. 🧠 You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience 🔧 You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

and wants to shape how AI is used in the physical world. 🧠 You’ll need: 2+ years' engineering experience with a builder’s mindset Hands-on with LLMs — LangChain, RAG, agents, fine-tuning, etc. Infra knowledge (cloud, containers, relational/graph DBs) Bonus: startup/founder experience 🔧 You’ll be doing: Shipping to prod from Day 1 Building end-to More ❯

technology and operations, with digital capabilities across all of these services. With our thought leadership and culture of innovation, we apply industry expertise, diverse skill sets and next-generation technology to each business challenge. We believe in inclusion and diversity and supporting the whole person. Our core values comprise of Stewardship, Best People, Client Value Creation, One Global … Vertex/Azure ML, OpenAI, Langchain, AutoML, OCR, STT, feature stores, vector DB Implementation of AI/ML architectural patterns and best practices e.g. data drift detection, experimentation tracking, RAG, deployment models Building and using cloud-based solutions (networking, security, storage, monitoring, scaling, DR/HA) on one or more cloud platforms SW Engineering and DevSecOps Set yourself apart: Certified More ❯

and help lay the foundations for their Agentic AI Journey. What you'll do: Architect and deploy robust, multi-agent systems using Google's ADK Design and implement advanced RAG pipelines to provide agents with deep contextual understanding Design, build, and maintain a suite of secure and scalable Model Context Protocol MCP Servers Own the full life cycle of agent … from initial design and prototyping to deployment, monitoring, and continuous improvement Experience required: Proven background in similar AI/ML Roles Strong proficiency in Python & SQL Demonstrable experience with RAG, Agentic AI, Googles ADK (Or similar LLM Application frameworks), API Design & Consumption Role details: AI Engineer Hybrid - 3 days p/w in Central London £60,000 - 75,000/ More ❯

and semantic reasoning. Leverage ontologies to enhance data pipelines and enable advanced knowledge engineering solutions. Collaborate with AI teams to ensure ontology structures efficiently support Agentic AI applications, including RAG pipelines and Agent Orchestration . Graph Database Expertise Work with both Semantic Graph and Property Graph technologies, understanding their unique architectures and use cases. Utilize Semantic Graph tools for rule … standards for Property Graphs. Ability to mentor and train junior team members, fostering a culture of learning and growth. Experience integrating AI considerations (like LLM-based retrieval, RAG pipelines, and Agent Orchestration) into graph ecosystem design. Preferred Qualifications Experience with data pipelines and integrating graph databases into larger data ecosystems. Knowledge of rule-based inference systems and network More ❯