1 to 25 of 36 Permanent Retrieval-Augmented Generation Jobs in the City of London

Agentic AI & LLM Engineering Agentic Workflow Design: Develop multi-agent systems using frameworks like LangGraph or CrewAI for autonomous research and automated coding. Advanced RAG & Memory: Build sophisticated Retrieval-Augmented Generation (RAG) pipelines for long-term model memory. Model Optimization: Fine-tune open-source … with AWS Bedrock/SageMaker; Docker/Kubernetes for scaling agent instances. Proven experience building Retrieval-Augmented Generation (RAG) systems; Graph-RAG experience is a plus. Practical experience with model adaptation techniques, including prompting, fine-tuning, instruction tuning, or knowledge distillation. Solid understanding ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

with the team to develop GenAI proof-of-concepts (POCs) for clients using technologies like Retrieval-Augmented Generation (RAG) and intelligent agents. Scale existing POCs to production-ready solutions for customer use. Design and develop Full Stack applications for both GenAI and non-GenAI ...

help drive innovation across cutting-edge AI initiatives — from chatbots and voice assistants to advanced retrieval-augmented generation (RAG) systems and agentic workflows. The Role You’ll work closely with the AI Engineering and Data Science teams to: Develop and prototype AI-driven solutions … across customer-facing and internal applications. Build and optimise LLM-based assistants , RAG pipelines , and agentic AI workflows . Collaborate on the architecture and deployment of scalable AI solutions (with support from engineering). Partner with stakeholders to translate business needs into practical, intelligent systems. Mentor junior team members ...

scale machine learning algorithms and pipelines to production using big data technologies. Develop and deploy retrieval-augmented generation (RAG) systems and LLM-based applications. Design and evaluate A/B tests and communicate results across cross-functional teams. Define, implement, and monitor key performance ...

managing multi-API workflows using tools like LiteLLM to ensure flexibility and resilience. Building sophisticated Retrieval-Augmented Generation (RAG) systems, leveraging advanced techniques like embeddings with Voyage AI, rerankers , and query enrichment . Designing and maintaining efficient data pipelines and vector storage solutions using … PyTorch. Proven experience building and deploying applications with LLM APIs such as OpenAI , Anthropic , Gemini , and DeepSeek . Hands-on experience with the full RAG pipeline, including vector embeddings , rerankers , and data indexing in databases like MongoDB. Practical knowledge of LLM fine-tuning, prompt engineering, and performance optimization. Familiarity with ...

create high-quality pre-trained datasets, fine-tuned industry-specific Large Language Models(LLMs), and Retrieval-Augmented Generation (RAG) pipelines supported by vector databases. Our innovations can reduce Generative Artificial Intelligence(Gen AI) costs by up to 80% and bring Gen AI solutions ...

LangChain or Semantic Kernel. Solid understanding of ML concepts (supervised/unsupervised learning, transformers, CNNs/RNNs, model evaluation). Experience with prompt engineering, RAG pipelines, and model fine-tuning. The judgement to identify where AI adds value — and where it doesn’t. Experience deploying and operationalising LLMs (exposure ...

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 ...

model training to inference APIs and monitoring. Integrate and scale LLMs & generative AI within product workflows, including fine-tuning, prompt engineering, RAG (retrieval-augmented generation), and agentic systems. Collaborate with product, backend, and frontend teams to deliver impactful AI-driven experiences. Take ownership ...

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 ...

architecture, frontend/backend patterns, and platform onboarding Mentoring engineers and driving best practices across teams Integrating modern AI tooling (LLMs, vector databases, RAG workflows) into internal platforms What we’re looking for 6+ years experience as a software or platform engineer in complex environments Strong Python engineering background (compiled ...

autonomous AI What You’ll Need: A strong foundation in data science and machine learning Hands-on use of modern AI tools (LLMs, RAG, LangChain, co-pilots, agentic workflows) Curiosity and eagerness to learn in a fast-moving AI landscape Experience collaborating with stakeholders and translating business problems into technical ...

Next.js, React, TypeScript, Python, FastAPI, Postgres, Redis Experience with cloud infrastructure (AWS, GCP) Exposure to or experience with foundation models or Generative AI (LLMs, RAG, fine-tuning, agents, evals, AI IDE, etc.) Experience working directly with users to iterate on products Benefits Early equity in a YC-backed, high-growth ...

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 ...

shipping reliable systems used by real customers. Key responsibilities Build and deploy LLM-powered features across drafting, prosecution, and litigation workflows Design and maintain RAG pipelines over large, complex legal and technical corpora Own systems from prototype through to production Focus on robustness, evaluation, and reliability in precision-critical ...

logic that allows an LLM to detect its own hallucinations in a workflow and force a re-plan based on ingested technical documentation. Shave RAG query times down from "noticeable lag" to "instant" across massive, unstructured enterprise datasets. Why a Senior Engineer would join this team You’ll work directly ...

Details • Salary: £80k–£100k base (flexible for strong profiles) • Working model: Hybrid, 3 days per week in London office • Tech stack: Python, JavaScript, GenAI (RAG, LangChain, LlamaIndex), agentic systems, Azure/AWS • Visa: No sponsorship available Interested? Please apply below. ...

assessments, and delight users Work with industry experts to turn messy claim processes into AI-driven workflows Deploy solutions using LLMs, LangChain, and RAG at scale What we're looking for 2+ years in AI/ML engineering or research Proven track record taking projects from idea → production → impact Bias ...

development, along with experience in Docker , PostgreSQL , or DSL/Compilers . Professional or enthusiastic personal experience with LLMs and their ecosystems (e.g., Langchain, RAG, or observability tooling). You are a "creative experimentalist" who can balance the need for speed with long-term engineering maintenance. A commitment to building ...

ambiguity, managing risk, and maintaining delivery momentum. Exposure to regulated or high-trust domains (Legal, HR, Finance, Healthcare) strongly preferred. Familiarity with Generative AI, RAG, or ML systems advantageous. Why Join? You'll play a pivotal role in ensuring Generative AI systems are delivered coherently, responsibly, and at scale-working ...

production (using e.g. OpenAI, Anthropic, Google, etc.) Proficiency with LLM infra platforms (prompt management, logging/tracing, evals) Experience designing large-context LLM systems (RAG, knowledge graphs, hybrid search, memory) Building features end-to-end with TypeScript, React.js, and Node.js As a person, you... Are first and foremost a builder. ...

similar engineering languages is a must Experience working with APIs, cloud infrastructure and data processing. Previous experience within AI tech such as LLMs, RAG, embedding, vector databases is a big plus as is any previous experience within an AI, Data or B2B SaaS company. This is a great time ...

want to build production GenAI products (not prototypes) in a business with real scale and board-level visibility? Have you shipped LLM/RAG systems end-to-end and owned them in production? Are you ready to join a new AI team early and shape how AI is delivered across … services that directly improve customer experience and business performance. Key responsibilities Design, build and deploy end-to-end AI solutions Develop LLM solutions (e.g. RAG, workflow orchestration, evaluation) Build APIs/services integrating data sources and AI outputs Translate stakeholder needs into practical technical delivery plans Own delivery quality: reliability ...

Senior LLM, RAG & Agentic AI Consulting Engineer - Lead/Senior FDE Remote First, some trips to client offices and HQ Lead the design and delivery of complex, AI-native client engagements, spanning agentic systems, retrieval architectures and semantic layers. This is a senior, hands-on consulting role combining … consulting engineering role. Candidates should bring: Solid experience in software engineering, AI engineering, or applied data engineering Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector databases Experience designing or implementing multi-agent systems or tool-calling frameworks Strong Python skills with experience building production ...