1 to 25 of 36 Retrieval-Augmented Generation Jobs in the South East

AI expert ready to take your skills to the next level? Do words like Azure OpenAI, Cognitive Services, prompt engineering, Retrieval-Augmented Generation (RAG) architectures, vector stores, and API integrations make you light up inside? If so, we want to hear from you! At 1st Central , we're on an exciting journey with AI … develop AI and Generative AI solutions using services like Azure OpenAI and Azure Cognitive Services Implement prompt engineering techniques and Retrieval-Augmented Generation (RAG) architectures. Ensure scalability, security, auditability, and efficiency of AI solutions through detailed system design and development practices. Deploy and manage AI solutions via CI/CD pipelines in Azure DevOps … deploying, and managing production-grade AI and Generative AI systems. Extensive experience with Cloud-based AI and Cognitive Services, and Retrieval-Augmented Generation (RAG) architectures. Deep expertise in API integration, preferably within the Azure ecosystem. Experience with Infrastructure as Code (IaC) across development, testing, and production environments. Solid understanding of Azure networking principles, security More ❯

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 ❯

problem-solving skills and the ability to work autonomously in a dynamic environment. DESIRABLE SKILLS Experience supporting LLM training or retrieval-augmented generation (RAG). Familiarity with feature stores, vector databases, or embedding pipelines. Understanding of data quality, lineage, and governance best practices. Exposure to cloud platforms (AWS, Azure, or GCP) and distributed compute 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 ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

cloud platforms and frameworks such as Azure AI/ML Studio, AWS Bedrock, GCP Vertex, Spark, TensorFlow, PyTorch, etc. Build and deploy production grade fine-tuned LLMs and complex RAG architectures. Create and manage the complex and robust prompts across the GenAI solutions. Communicate effectively with stakeholders and colleagues, using data visualisation, storytelling, and presentation skills. Ensure the ethical use More ❯

is an exciting opportunity to work in a greenfield environment, where you will play a crucial role in designing and deploying secure, scalable AI services that drive next-generation use cases, including client intelligence, document processing, and risk management. Key Responsibilities: Architect & Implement: Design and deploy secure AI services from lab to … production, ensuring they are scalable and compliant with industry standards. API Development: Create robust APIs for large language models (LLMs), retrieval-augmented generation (RAG) pipelines, agentic workflows, and document intelligence systems. Cybersecurity & Privacy: Integrate cybersecurity and data privacy controls across all AI workflows, including encryption, anonymisation, and access logging. Collaborate with CISO: Work closely … workflows using modular LLM agents with memory, planning, and tool integration. Experience implementing Model Context Protocol (MCP) for secure, auditable context injection across agentic systems. Demonstrated ability to build RAG pipelines with strict data governance and contextual integrity. Familiarity with regulatory frameworks such as the EU AI Act, FCA cybersecurity principles, and oversight of critical systems. Previous collaboration with cybersecurity More ❯

ML model development, deployment, and running cloud-native AI systems in production. Generative AI skills - experience with large language models (LLMs) such as GPT-4/5, fine-tuning, RAG pipelines, or AI-powered chatbots. MLOps and integration - experience with frameworks like Kubeflow, MLflow, or Vertex AI, and deploying models behind APIs with monitoring and feedback loops. AI guardrails & quality More ❯

secure cloud development practices and IAM role design. Understanding of LLM fine-tuning, embeddings, vector stores (e.g., Pinecone, FAISS, OpenSearch). Exposure to contact centre automation, conversational agents, or RAG pipelines. Adecco is a disability-confident employer. It is important to us that we run an inclusive and accessible recruitment process to support candidates of all backgrounds and all abilities More ❯

team building scalable GenAI solutions, architect Agentic AI components, and integrate them into enterprise systems.Skills- Strong experience in full-stack and cloud-native engineering (Azure preferred) Expertise in GenAI (RAG, prompt engineering, LLM APIs) Deep knowledge of Agentic AI systems and orchestration Proficiency in Python, SQL, NodeJS (React is a plus) Agile leadership and stakeholder collaboration skills Details- 6 month 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 ❯

MVPs, then production ready services. - Develop, package and maintain shared Python libraries; ensuring robust and clean architecture, testing and CI/CD standards. - Design and deploy LLM based extraction, RAG and agentic workflows over large document collections, and drive value through integration of latest AI tech (agentic workflows etc.) - ️ Communicate clearly with technical and non technical stakeholders. Required Skills & Experience 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 ❯

optimized for complex financial workflows Event-driven distributed systems handling sensitive data at enterprise scale The Tech Stack AI/ML : Multi-agent frameworks, fine-tuning, reinforcement learning, advanced RAG Platform : AWS serverless, event-driven architecture, microservices Languages : Python, with modern ML libraries (PyTorch, TensorFlow) What They're Looking For 5+ years developing AI/ML systems beyond basic integration More ❯

backend engineering , ideally with Go and ML frameworks like PyTorch or TensorFlow Familiarity with MLOps , cloud infrastructure (AWS) , Kubernetes , and Terraform Experience evaluating and deploying models (including anomaly detection, RAG, and clustering) in noisy, evolving data environments Nice to Have: Experience with Perl Knowledge of threat intelligence integration and MCP architectures Location: Remote Salary: Up to £120,000 , depending on 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 ❯

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 ❯

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 ❯

data team and creating a culture that enables growth, development and psychological safety Exploring and applying Large Language Models (LLMs) and other modern AI techniques (e.g., embeddings, retrieval-augmented generation, summarisation) to enhance internal tools, automate workflows, or develop client-facing features Acquiring, cleaning, and integrating data from internal and external sources to enrich More ❯

data team and creating a culture that enables growth, development and psychological safety Exploring and applying Large Language Models (LLMs) and other modern AI techniques (e.g., embeddings, retrieval-augmented generation, summarisation) to enhance internal tools, automate workflows, or develop client-facing features Acquiring, cleaning, and integrating data from internal and external sources to enrich 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 ❯

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 ❯