22 of 22 Permanent Retrieval-Augmented Generation Jobs in the City of London

Master prompt engineering, learning how to craft effective prompts for controlling AI outputs. Step 9 – Retrieval-Augmented Generation (RAG) Learn how to integrate external knowledge into AI systems using RAG techniques and vector databases. Step 10 – AI Specialised Customer Service Chatbot Project Combine prompt … engineering and RAG to build an AI-powered customer service chatbot, delivering intelligent responses using vector databases and knowledge bases. Step 11 – Machine Learning Fundamentals Understand machine learning principles and algorithms, and how to train and test models using scikit-learn. Step 12 – Machine Learning Project Put your machine learning ...

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 ...

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 ...

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 ...

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 ...

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 ...

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). ...

They’re Looking For Proven experience designing & deploying AI/ML solutions Expert knowledge of LLMs, AI frameworks & agent-based systems Experience in building RAG Systems Cloud expertise across AWS/Azure/GCP Ability to turn complex problems into scalable architectures Passion for AI innovation (this ...

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 ...

things Comfortable working in a more entrepreneurial, less structured environment Strong academic background preferred Useful experience may include: FastAPI, React, TypeScript, Streamlit, APIs LLMs, RAG, agentic workflows, automation tools Internal tools, dashboards, workflow systems, CRM-type platforms AI-enabled process improvement or business automation This is a particularly strong opportunity ...

React/TypeScript frontend fundamentals with solid backend experience (language is not a constraint). - Practical, hands-on knowledge of AI application development: LLMs, RAG architectures, AI agents, and evaluation approaches. - Excellent client-facing communication skills — comfortable in a room with VP-level stakeholders, able to translate technical depth into ...

TypeScript Contribute to scalable, distributed systems and cloud architecture (AWS) Work with structured and unstructured data across varied storage solutions Build and integrate RAG pipelines and AI-driven workflows Implement LLM integration patterns and multi-step orchestration Collaborate with cross-functional teams to deliver robust, high-quality systems What They ...

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 ...

software engineering principles (Python preferred) Business-focused mindset, able to evaluate AI initiatives through an ROI lens Cloud and AI knowledge, including LLMs, RAG, prompt engineering, and modern AI tooling Solid Python knowledge Familiarity with Claude and similar frontier models Production experience using LangChain …/ML or similar discipline Desirable RAG architectures, embeddings, and vector databases Prompt management, evaluation, and observability Cloud deployment (AWS, Azure, or GCP) Enterprise concerns: security, governance, and cost control Strong stakeholder communication and technical decision-making ...

delivery of scalable, resilient AI/ML pipelines aligned to business goals. Build and deploy advanced ML, DL, and LLM solutions, including RAG, agentic architectures, and evaluation frameworks. Mentor engineers in ML engineering, orchestration, and modern AI best practices. Communicate system behaviours, risks, and outcomes to both technical … Partner with the Head of AI Labs to turn AI research and strategy into production‐ready solutions. Key Requirements: Proven experience designing and productionising RAG pipelines and agentic AI architectures Hands‐on delivery of agent‐based AI systems with orchestration tools (e.g., LangChain, CrewAI) in production. Strong Python engineering skills ...

environment where experimentation, iteration, and innovation are actively encouraged. What You’ll Be Doing Designing and building production-grade AI systems using LLMs Developing RAG pipelines, vector search solutions, and agent-based workflows Creating robust evaluation frameworks to measure accuracy, reliability, and performance Building scalable APIs, services, and data pipelines … What We’re Looking For Experience building applications using LLM APIs, with a strong understanding of their strengths and limitations Hands-on experience with RAG architectures, vector databases, and prompt engineering Experience developing multi-step workflows or agentic systems (frameworks or custom builds) Strong programming skills (Python or similar), with ...

lasting around nine months, building AI applications that solve real operational problems. Key Responsibilities • Design and implement LLM-powered applications for enterprise clients • Build RAG pipelines and AI workflows integrating enterprise data • Develop production APIs and backend services in Python • Implement evaluation frameworks and monitoring for AI systems • Optimise … squads Key Details • Salary: £75,000 – £90,000 • Working model: Hybrid (2–3 days per week in Central London) • Tech stack: Python, LLM APIs, RAG architectures, vector databases, agent frameworks, cloud platforms (AWS/GCP/Azure) • Visa sponsorship: Not available Interested? Please apply below. ...

/B experiments and performance dashboards. • Integrate AI agent functionality with applications through MCP servers and reliable tool configurations. • Develop secure and scalable RAG pipelines, including ingestion, text processing, metadata handling and vector search in GCP. • Embed security, compliance, GDPR controls and best‐practice engineering with clear documentation and stakeholder … Strong Python skills for backend services, SDKs, API development, authentication and testing. • Hands‐on experience working with LLMs, embeddings, prompt engineering, tool calling and RAG design. • Practical knowledge of GCP services including Vertex AI, Model Garden, BigQuery, Cloud Run and Google’s ADK. • Experience with document and data processing using ...

join at the foundational stage, working directly with world‐class engineers and product leaders to build the backend systems behind a next‐generation AI lifestyle platform. This product is already in the hands of deeply engaged early users, with engagement metrics far above industry norms. You will play … delivery of key features—from design to production rollout. Develop background processing systems, task queues, and agent orchestration. Work hands‐on with LLM integrations, RAG pipelines, and AI‐driven systems. Enhance system performance across SQL queries, APIs, and distributed jobs. Build and operate cloud infrastructure on GCP. Lead debugging ...

build a high-conversion sales pipeline. Al Revenue Ownership: Own the full lifecycle of Al Maas opportunities in international markets, from lead generation and qualification to solution design, commercial negotiation, and deal closure, driving growth in token consumption and revenue. Market & GTM Strategy Support: Track GenAl trends (foundation … pricing, and contracting, while collaborating with solution architects and product teams Cloud Experience: Familiar with cloud and AI infrastructure products (e.g., servers, GPU, MaaS, RAG, Agents); hands-on experience in cloud sales, business consulting, and operations. Mindset: Fast learner, customer-obsessed, results-driven, self-motivated, and passionate about Al innovation ...