1 to 25 of 38 Retrieval-Augmented Generation Jobs in the South East

Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for … Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and More ❯

Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for … Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and More ❯

Responsibilities Architect Autonomous Agents: Design and implement robust, goal-driven AI agents using leading frameworks like LangChain, LangGraph, and the Google Agent Development Kit (ADK). Develop and Evaluate RAG Pipelines: Engineer and optimize end-to-end Retrieval-Augmented Generation (RAG) systems, including data ingestion, chunking strategies, and implementing rigorous pipeline evaluation frameworks for … Kit (ADK) LLM Expertise: Advanced Prompt Engineering and hands-on experience with model fine-tuning techniques including PEFT and QLoRA. Proven experience with models like Gemini, and Llama 3. RAG & Vector Databases: Deep expertise in RAG architecture and evaluation metrics. Proven experience with Vector Databases such as Milvus, Pinecone, or Chroma. Software & Cloud Engineering: Programming & APIs: Expert-level Python and More ❯

projects, driving timelines, and delivering high-quality results in fast-paced environments. Bonus: Experience in deploying multi-agent frameworks and retrieval-augmented generation (RAG) pipelines. Familiarity with regulatory and privacy considerations in healthcare AI applications. IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences More ❯

projects, driving timelines, and delivering high-quality results in fast-paced environments. Bonus: Experience in deploying multi-agent frameworks and retrieval-augmented generation (RAG) pipelines. Familiarity with regulatory and privacy considerations in healthcare AI applications. IQVIA is a leading global provider of clinical research services, commercial insights and healthcare intelligence to the life sciences More ❯

personal and professional development! Requirements: Strong 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 ❯

personal and professional development! Requirements: Strong 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 ❯

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 ❯

dependable and consistent results Create and implement intelligent workflow systems, integrating LLMs with enterprise applications, APIs, and automation ecosystems Develop Retrieval-Augmented Generation (RAG) architectures to connect organisational knowledge with LLMs Rapidly prototype and refine AI applications, showcasing tangible business benefits Provide guidance on ethical AI implementation, covering governance frameworks, regulatory compliance, and risk … JavaScript for rapid prototyping and system integration Familiarity with workflow automation solutions (UiPath, Power Automate, Zapier, n8n) and API development Practical knowledge of vector databases and embedding technologies for RAG system implementation Outstanding interpersonal skills - capable of converting business challenges into technical AI strategies Background in agile methodologies or dynamic project environments **Please only apply for this role if you 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 ❯

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 ❯

third-party services. Develop front-end interfaces using JavaScript and frameworks such as React. Build and deploy Agentic systems and Retrieval-Augmented Generation (RAG) systems with Large Language Models (LLMs). Participate in privacy automation programs, including workflows for user approvals and reviews. Implement and maintain CI/CD pipelines in Jenkins to streamline More ❯

time management skills. Bonus skills • Experience deploying and managing applications with Azure and Docker. • Familiarity with frameworks like LangChain and Retrieval-Augmented Generation (RAG) models for AI-driven applications. • Experience with pandas for data manipulation. What’s on offer • Regular salary reviews recognising performance and contribution. • Generous annual leave: 25 days plus three days More ❯

dimensional modelling to deliver consistent, trusted analytics. Enable advanced AI and ML use cases by building pipelines for vector search, retrieval-augmented generation (RAG), feature engineering, and model deployment. Ensure security and governance through robust access controls, including RBAC, SSO, token policies, and pseudonymisation frameworks. Develop resilient data flows for both batch and streaming More ❯

designing and managing databases in Postgres. AI/ML Expertise: Build and deploy Agentic systems, MCP servers/clients, and Retrieval-Augmented Generation (RAG) systems with Large Language Models (LLMs). Extract, transform, and load (ETL) data to build vector databases with embedding models. DevOps and Maintenance: Implement CI/CD pipelines to streamline More ❯

huggingface and LLM offerings by 3rd party vendors. Knowledge of OO programming and software design, i.e., SOLID principles. Knowledge and working experience of AGILE methodologies. Familiarity with Databricks, and RAG application architecture a plus Experience with latency optimisation and quantisation preferred Additional Information What’s in it for you?: Competitive salary that reflects your skills, experience and potential. Discretionary bonus 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 ❯

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. utilise More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

to junior consultants Supporting Microsoft partnership activities and training sessions Requirements: Proven experience with Microsoft 365 Copilot, Copilot Studio, and related technologies Strong understanding of AI concepts including LLMs, RAG, and prompt engineering Familiarity with Azure services and cloud ecosystems Excellent communication and presentation skills A passion for mentoring and developing engineering talent Experience with distributed systems and incident response More ❯

paced environment. Hands-on with Next.js, Typescript (React/Node), Tailwind, SQL (Prisma), and Azure . Bonus points for experience with: LLM APIs (OpenAI, Anthropic), Retrieval Augmented Generation pipelines, Python, serverless architecture, or Microsoft Office Add-ins. Strong product mindset and passion for building. You may have tried a startup yourself or hacked together More ❯

AI pipelines. What You'll Bring Deep solution architecture experience across cloud, data, and business platforms. Proven experience designing or integrating AI/GenAI solutions such as copilots, chatbots, RAG systems, or automation tools. Understanding of GenAI and agentic design patterns, including orchestration frameworks and RAG. Experience with event-driven systems, messaging platforms, and APIs. Familiarity with AI governance standards More ❯

tools for modelling. Working experience on Modern data platforms which involves Cloud & related technologies Keeps track of industry trends and incorporates new concepts Advanced understanding of AI concepts , LLMS, RAG, Agentic apps Advance Understanding in Data visualization and Data pipelines. Excellent understanding of traditional and distributed computing paradigm. Experience in designing and building scalable data pipelines. Should have excellent knowledge More ❯