1 to 25 of 27 Retrieval-Augmented Generation Jobs in the South East

experts, and UX developers. The role involves: Prototyping AI/NLP tools for policy analysis and consultation feedback processing Building retrieval-augmented generation (RAG) systems to provide policy guidance Preparing datasets and pipelines using structured and unstructured regulatory data Contributing to model evaluation and iteration based on user feedback Developing prototype tools to support … as a short-term contract, whereas a more well-rounded candidate could fit longer-term needs. Desirable Skills Familiarity with retrieval-augmented generation (RAG) architectures Experience with front-end data visualisation Understanding of UAS (drones), aviation, or other regulated industries Involvement in agile software development environments Strong communication skills, particularly the ability to explain More ❯

Certain Advantage are recruiting on behalf of our Trading client for an AI Data Scientist who can bring a strong understanding in modern NLP, LLMs, transformer architectures, prompt-engineering, RAG, agentic architectures and evaluation methodologies.They require candidates to offer strong knowledge of Python programming for developing and debugging AI models and would expect suitable candidates to be educated to an … within the GenAI/NLP team. This role will focus on the application and development of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and domain-specific GenAI solutions to support key internal use cases and products. Responsibilities In this role you will: Design, implement and maintain scalable NLP and GenAI pipelines (including … to date with state-of-the-art research in the space of LLMs/NLP, proposing new ideas and methodologies that unlock business value. Contribute to the development of RAG systems and retrieval pipelines, including chunking, embedding, re-ranking, and evaluation. Participate in experiments, including designing experimental details, writing reusable code, running evaluations, and organising results. Collaborate with More ❯

professional development! Requirements: Strong skills in Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects Experience with Retrieval-Augmented Generation (RAG) Experience with Microsoft data technologies would be beneficial Experience with Cloud platforms - ideally Azure Strong communication, stakeholder management and problem-solving skills Benefits: Salary of up to around More ❯

professional development! Requirements: Strong skills in Python scripting skills Strong understanding of LLMs Experience delivering Gen-AI projects Experience with Retrieval-Augmented Generation (RAG) Experience with Microsoft data technologies would be beneficial Experience with Cloud platforms - ideally Azure Strong communication, stakeholder management and problem-solving skills Benefits: Salary of up to around More ❯

and love building innovative solutions from scratch, this could be the perfect role for you. What you'll be doing Building and refining LLM-based agents, including retrieval-augmented generation and data retrieval systems. Designing AI-powered system architecture using Python, Java, and SQL/NoSQL databases. Fine-tuning models (LLMs & SLMs … Essential: Strong experience with Gen AI, NLP, and machine learning. Proven ability to create, train, and fine-tune LLMs/SLMs. Skills in prompt engineering and RAG (retrieval-augmented generation). Solid background in Python, Java, and database technologies (SQL/NoSQL). Experience in crawling/web scraping and API development. Experience with More ❯

accuracy and trust. Responsibilities: Fine-tune and optimize foundation models (e.g., LLaMA, FinBERT) for financial text and transaction data. Build retrieval-augmented generation (RAG) pipelines with Hugging Face, Lang Chain, and vector databases. Implement human-in-the-loop systems to continuously validate, improve, and govern AI outputs. Deploy and monitor ML models in production More ❯

in a distributed team environment. Customer-first mindset with a data-driven approach to problem-solving. Experience with large-scale distributed systems and incident response. Familiarity with LLM technologies (RAG, prompt engineering, evaluation methods). Knowledge of Azure services and Microsoft's ecosystem. Ability to work independently and collaboratively. Degree in Data Science, Computer Science, or related field (preferred). More ❯

Architect multi-step agent workflows using: Semantic Kernel SDK (C# or Python) Azure OpenAI (GPT-4, function calling, chat completion) Planner and Kernel Memory APIs for reasoning and memory RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in: SharePoint, OneDrive, Teams, Outlook, and Planner Use More ❯

Architect multi-step agent workflows using: - Semantic Kernel SDK (C# or Python) - Azure OpenAI (GPT-4, function calling, chat completion) - Planner and Kernel Memory APIs for reasoning and memory - RAG pipelines grounded in enterprise data via Azure AI Search Microsoft 365 & Graph API Integration Enable agents to access and reason over content in SharePoint, OneDrive, Teams, Outlook, and Planner. utilise More ❯

data science solutions Apply statistical modelling, machine learning, and programming expertise to solve complex problems Explore and implement modern AI technologies including large language models, retrieval augmented generation, and advanced automation tools Deliver production ready solutions that integrate into existing client environments Engage with stakeholders at different levels, translating technical approaches into clear business More ❯

data science solutions Apply statistical modelling, machine learning, and programming expertise to solve complex problems Explore and implement modern AI technologies including large language models, retrieval augmented generation, and advanced automation tools Deliver production ready solutions that integrate into existing client environments Engage with stakeholders at different levels, translating technical approaches into clear business More ❯

data science solutions Apply statistical modelling, machine learning, and programming expertise to solve complex problems Explore and implement modern AI technologies including large language models, retrieval augmented generation, and advanced automation tools Deliver production ready solutions that integrate into existing client environments Engage with stakeholders at different levels, translating technical approaches into clear business More ❯

to solve complex business challenges.IR 35 Status - Inside Key Responsibilities Design, build, and deploy Python-based AI applications. Research and prototype advanced GenAI techniques, including fine-tuning, prompt engineering, RAG, and agentic systems. Develop LLM-assisted frameworks and contribute to scalable AI architectures. Write clean, maintainable, and well-documented code. Implement testing pipelines and evaluation frameworks. Collaborate with data scientists More ❯

Natural Language Processing (NLP) Hands-on experience in at least one of the following areas: Embedding-based retrieval systems Representation learning using deep learning Reliable LLM/RAG/Agentic systems Bonus Points: Experience with fine-tuning embedding models Published papers in NLP, representation learning, or related fields Familiarity with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience More ❯

Natural Language Processing (NLP) Hands-on experience in at least one of the following areas: Embedding-based retrieval systems Representation learning using deep learning Reliable LLM/RAG/Agentic systems Bonus Points: Experience with fine-tuning embedding models Published papers in NLP, representation learning, or related fields Familiarity with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience More ❯

Natural Language Processing (NLP) Hands-on experience in at least one of the following areas: Embedding-based retrieval systems Representation learning using deep learning Reliable LLM/RAG/Agentic systems Bonus Points: Experience with fine-tuning embedding models Published papers in NLP, representation learning, or related fields Familiarity with modern ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience More ❯

in startup environments Strong background in Data or Software Engineering (not Data Science) Experience building and deploying AI pipelines Key Skills Python LangChain/LangGraph/CrewAI LLMs TensorFlow RAG Systems LlamaIndex Additional Info Must have full right to work and live in the UK Immediate start preferred More ❯

with the bank's cybersecurity and regulatory standards. Accountabilities & Responsibilities Architect and implement secure AI services from lab to production, ensuring scalability and compliance Develop robust APIs for LLMs, RAG pipelines, agentic workflows and document intelligence systems Embed cybersecurity and data privacy controls across all AI workflows (e.g., encryption, anonymisation, access logging) Collaborate with the CISO function on threat modeling … Built agentic workflows using modular LLM agents with memory, planning, and tool integration Implemented Model Context Protocol (MCP) to manage secure, auditable context injection across agentic systems Experience building RAG pipelines with strict data governance and contextual integrity Familiarity with EU AI Act, FCA cybersecurity principles, and oversight of critical systems Worked directly with cybersecurity and compliance teams in regulated … LLM Fine-tuning and prompt engineering for LLMs (e.g., GPT, Gemini, Claude) Secure deployment of LLMs via APIs with input/output filtering and logging Integration of LLMs into RAG pipelines, document intelligence, and agentic workflows Use of vector databases (e.g., FAISS, Pinecone, Chroma) for semantic search and retrieval Implementation of grounding, context injection, and response validation mechanisms More ❯

Responsibilities Collaborate with enterprise clients to understand requirements and translate them into scalable AI solutions Design and implement production ready systems using large language models, retrieval augmented generation, and agentic workflows Lead the delivery of AI products from concept through to deployment and adoption Engage with both technical teams and business stakeholders, ensuring solutions More ❯

Responsibilities Collaborate with enterprise clients to understand requirements and translate them into scalable AI solutions Design and implement production ready systems using large language models, retrieval augmented generation, and agentic workflows Lead the delivery of AI products from concept through to deployment and adoption Engage with both technical teams and business stakeholders, ensuring solutions More ❯

Responsibilities Collaborate with enterprise clients to understand requirements and translate them into scalable AI solutions Design and implement production ready systems using large language models, retrieval augmented generation, and agentic workflows Lead the delivery of AI products from concept through to deployment and adoption Engage with both technical teams and business stakeholders, ensuring solutions More ❯

Proven experience delivering GenAI or automation solutions 10+ years of business experience, with 5+ years as a product lead or senior business analyst Strong grasp of GenAI stack (prompting, RAG, orchestration, unstructured data) Product mindset with a focus on speed to value Ability to navigate ambiguity and influence stakeholders Carbon60, Lorien & SRG - The Impellam Group STEM Portfolio are acting as More ❯

delivery innovations. Key Responsibilities: Design and deploy Python-based AI applications for internal use Conduct R&D on generative AI techniques including fine-tuning, prompt engineering, and retrieval-augmented generation Build agentic systems and LLM-assisted frameworks using modern orchestration tools Collaborate with cross-functional teams to deliver production-ready solutions Maintain high standards … as Numpy, Pandas, Scikit-Learn, Langchain, LlamaIndex Deep expertise in Azure and its AI services, including Azure AI Foundry Hands-on experience with GenAI techniques: prompt orchestration, retrieval methods (RAG, knowledge graphs), agentic frameworks (LangGraph, Semantic Kernel Agents) Familiarity with software engineering best practices: version control, testing, deployment Ability to collaborate with AI engineers and translate prototypes into More ❯

structure with the central AI team, MarTech, Data, and Compliance. THE ROLE: AI roadmap for marketing communications and engagement. Own key products: conversational assistants, content generation, and RAG search. Take each product from discovery to launch and scale. Build “governance-lite” controls: usage logging, prompt review, risk tiers, data rules. Align with enterprise AI policy, privacy, and regulatory … to ship MVPs fast. THE CANDIDATE'S EXPERIENCE: End-to-end ownership of an AI platform or capability used by ~1,000+ users. Strong knowledge of LLMs, conversational design, RAG, and prompt practices. Proven change leadership: you have driven ≥50% adoption with training and comms. Governance mindset: you have designed guardrails that pass risk and legal review. Working grasp of More ❯

structure with the central AI team, MarTech, Data, and Compliance. THE ROLE: AI roadmap for marketing communications and engagement. Own key products: conversational assistants, content generation, and RAG search. Take each product from discovery to launch and scale. Build “governance-lite” controls: usage logging, prompt review, risk tiers, data rules. Align with enterprise AI policy, privacy, and regulatory … to ship MVPs fast. THE CANDIDATE'S EXPERIENCE: End-to-end ownership of an AI platform or capability used by ~1,000+ users. Strong knowledge of LLMs, conversational design, RAG, and prompt practices. Proven change leadership: you have driven ≥50% adoption with training and comms. Governance mindset: you have designed guardrails that pass risk and legal review. Working grasp of More ❯