11 of 11 Part-Time Retrieval-Augmented Generation Jobs in the UK

building and optimizing Large Language Model (LLM) inferences and creating robust web services. This includes developing event-driven and request-response systems to run RAG (Retrieval-Augmented Generation) answer generation pipelines, essential for delivering sophisticated AI-driven solutions. Your role will require … an understanding of LLM frameworks such as Haystack, LlamaIndex, and LangChain, with a focus on Retrieval-Augmented Generation (RAG) and text/chat generators. Cloud computing with AWS (ECS, EKS, DynamoDB, Bedrock) Knowledge of git version control, branching, and code versioning. Passionate about code More ❯

IT Consultancy to develop and scale cutting-edge GenAI full-stack applications using technologies like Retrieval-Augmented Generation (RAG), intelligent agents, and LLMs. Salary - £55,000 per annum + Additional Benefits Remote with occasional client visits Candidates must be eligible for SC Clearance No … 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 FullStack applications for both GenAI and non-GenAI projects. More ❯

into crafting and optimizing sophisticated AI pipelines. This involves extensive prompt engineering, building and refining Retrieval-Augmented Generation (RAG) systems, and developing agentic AI workflows that deliver powerful analytical capabilities to our users. Master the Domain: Immerse yourself in the world of financial analysis. … You have demonstrable experience building and shipping meaningful projects (at work or independently) using modern AI tools and techniques (e.g., OpenAI, Anthropic, Gemini APIs; RAG, agent frameworks, prompt engineering). Showcasing this via a portfolio, open-source contributions (e.g., a GitHub repo), or detailed project walkthroughs is highly encouraged. Tinkerer More ❯

Engineer with full-stack development experience to work on cutting-edge projects involving Generative AI , Retrieval-Augmented Generation (RAG) , and multi-agent reasoning frameworks . This is a hands-on, end-to-end engineering role with impact across the full ML lifecycle – from experimentation … to deployment. Conversational AI & Reasoning: Design, fine-tune, and deploy advanced LLMs with agentic capabilities RAG Pipelines: Build and optimise scalable pipelines for structured and unstructured data retrieval LLM Training & Fine-Tuning: Use methods like LoRA, QLoRA, SFT, PEFT, and RLHF Inference & Acceleration: Serve models using vLLM, DeepSpeed … 5+ years of experience in ML engineering and software development Deep Python proficiency, with PyTorch, TensorFlow or Hugging Face Proven experience with LLMs, RAG, and deploying cloud-native AI on AWS Strong full-stack skills (React, TypeScript, Node.js) and API development Familiarity with vector databases and multi-agent frameworks Apply More ❯

in production. Strong background in LLMs, NLP, and conversational AI (e.g., GPT, Claude, Mistral, LLaMA, etc.) Expertise with frameworks like LangChain, Hugging Face, OpenAI, RAG pipelines, and vector databases (e.g., Weaviate, Pinecone, Chroma) Solid knowledge of AI system architecture, including model serving, monitoring, and optimization Strong programming skills in Python More ❯

API and database skills (e.g., PostgreSQL). ML/AI: ML model training and deployment expertise. AutoML know-how (e.g., AutoKeras). NLP and RAG skills (embedding, indexing). Familiarity with HuggingFace, LangChain. Deployment: LLM deployment (SGLang, TGI, vLLM). Linux/command-line fluency. Cloud deployment basics. Kubernetes/ More ❯

workflows Required Experience: Strong hands-on Python (production code, not just prototyping) Experience with DBT , Airflow , and general ETL/data engineering Exposure to RAG systems , NLP , and GenAI tooling Ability to build ML solutions that scale across large user bases (100k+) Demonstrated commercial impact (e.g., reducing churn, increasing MRR … Fully remote Start Date: ASAP If interested, please send your CV Desired Skills and Experience DBT Apache Airflow CI/CD RAG (Retrieval-Augmented Generation) ETL Pipelines SQL More ❯

Engineering skills (3 years+) Developed LLM architecture and deployed LLM applications Uptodate with current trends in AI Some experience with applying latest techniques like RAG architecture, GenAI, Parallel training etc The role is hybrid, with adhoc requirements to be on client premises (London) this could be between 1-5 days More ❯

Machine Learning Engineer - RAG Experience - Founding Engineer Machine Learning Engineer Required for a well funded Start-Up building an innovative tool and knowledge management solution to save consultancies considerable time. You will be looking to work for an early stage Start-Up working with a small … but world class team and play a critical role with your Python, Machine Learning skills and ideally exposure to Retrieval Augmented Generation (RAG). This is a critical hire that will need to work closely with the founders. Any experience building products from inception … will be highly beneficial. Currently interviewing - please send CV for immediate review! Machine Learning Engineer - RAG Experience - Founding Engineer More ❯

LLMs & Generative AI A leading UAE financial institution is expanding its AI capabilities with a dedicated AI & Data Science team focused on deploying LLMs, RAG, and Generative AI at scale. With several AI products already in production and a roadmap to launch 10+ AI-driven solutions in 2025, this role … scaling, and deploying AI models that deliver real business impact . Key Responsibilities Lead AI innovation with a focus on LLMs, Generative AI, and RAG for real-world applications. Drive end-to-end development and deployment of AI models from ideation to production . Build and scale an elite data … and customer experiences. Work cross-functionally to embed AI into core products and services. Stay ahead of advancements in LLMs, GenAI, and retrieval-augmented generation to ensure cutting-edge solutions. Define AI governance, scalability, and commercialization strategies for sustainable impact. Your Profile 12+ years More ❯

REST or GraphQL endpoints. •⁠ ⁠Build multi-tenant data models and role-based workflows. •⁠ ⁠Trigger emails and webhooks for status changes. *AI/Retrieval-Augmented Generation* •⁠ ⁠Wire existing prompts to an LLM API (OpenAI-compatible today, private model later). •⁠ ⁠Store embeddings in a vector … store and perform RAG look-ups. •⁠ ⁠Surface “explanation” JSON for every model decision. *Cloud hand-offs* •⁠ ⁠Deploy new services into the existing Terraform/ECS stack—extend, don’t rewrite. •⁠ ⁠Add CloudWatch alarms, log routes and parameter-store secrets where needed. *Quality & Ops* •⁠ ⁠Write unit and integration tests and set More ❯