1 to 25 of 34 Retrieval-Augmented Generation Jobs in the South East

real-world applications. Develop and optimise pipelines for data collection, preprocessing, and model evaluation. Collaborate with product engineers to deploy models into scalable production systems. Experiment with prompt engineering, RAG architectures, and multimodal models. Contribute to internal tools for monitoring, testing, and improving AI performance. Stay on the edge of ML/AI research — we give you time and resources … to PyTorch/TensorFlow/Hugging Face. (Bonus) understand fundamentals of deep learning, LLMs, and MLOps, vector databases, embeddings, or retrieval-augmented generation (RAG). Loves solving complex problems and thrives in a fast-moving startup environment. Is curious, ambitious, and eager to build real AI systems that have an impact. What You’ll … LLM Engineer, AI Engineer, Artificial Intelligence, Deep Learning, NLP, Natural Language Processing, Large Language Models, Generative AI, GenAI, Neural Networks, PyTorch, TensorFlow, Hugging Face, OpenAI, LangChain, RAG, Retrieval-Augmented Generation, Python, Data Science, AI Research, MLOps, Data Pipelines, Prompt Engineering, Model Fine-Tuning, Cloud Computing, AWS, Azure, Google Cloud, AI Infrastructure, Transformers, Reinforcement Learning More ❯

and frame AI use cases Prototype multi-agent systems, reasoning engines, and knowledge-graph integrations Implement and fine-tune models for language understanding, decision automation, and data insight generation Evaluate and integrate open-source and commercial AI frameworks into orchestration workflows Ensure AI solutions are explainable, auditable, and compliant with public- sector governance standards Advise on AI strategy … Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong analytical and problem-solving skills, with the ability to translate research into practical outcomes Excellent written and verbal … recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops memory systems governance frameworks public-sector AI standards BPMN More ❯

and frame AI use cases Prototype multi-agent systems, reasoning engines, and knowledge-graph integrations Implement and fine-tune models for language understanding, decision automation, and data insight generation Evaluate and integrate open-source and commercial AI frameworks into orchestration workflows Ensure AI solutions are explainable, auditable, and compliant with public- sector governance standards Advise on AI strategy … Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong analytical and problem-solving skills, with the ability to translate research into practical outcomes Excellent written and verbal … recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops memory systems governance frameworks public-sector AI standards BPMN More ❯

and frame AI use cases Prototype multi-agent systems, reasoning engines, and knowledge-graph integrations Implement and fine-tune models for language understanding, decision automation, and data insight generation Evaluate and integrate open-source and commercial AI frameworks into orchestration workflows Ensure AI solutions are explainable, auditable, and compliant with public- sector governance standards Advise on AI strategy … Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong analytical and problem-solving skills, with the ability to translate research into practical outcomes Excellent written and verbal … recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops memory systems governance frameworks public-sector AI standards BPMN More ❯

and frame AI use cases Prototype multi-agent systems, reasoning engines, and knowledge-graph integrations Implement and fine-tune models for language understanding, decision automation, and data insight generation Evaluate and integrate open-source and commercial AI frameworks into orchestration workflows Ensure AI solutions are explainable, auditable, and compliant with public- sector governance standards Advise on AI strategy … Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong analytical and problem-solving skills, with the ability to translate research into practical outcomes Excellent written and verbal … recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops memory systems governance frameworks public-sector AI standards BPMN More ❯

and frame AI use cases Prototype multi-agent systems, reasoning engines, and knowledge-graph integrations Implement and fine-tune models for language understanding, decision automation, and data insight generation Evaluate and integrate open-source and commercial AI frameworks into orchestration workflows Ensure AI solutions are explainable, auditable, and compliant with public- sector governance standards Advise on AI strategy … Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong analytical and problem-solving skills, with the ability to translate research into practical outcomes Excellent written and verbal … recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops memory systems governance frameworks public-sector AI standards BPMN More ❯

and frame AI use cases Prototype multi-agent systems, reasoning engines, and knowledge-graph integrations Implement and fine-tune models for language understanding, decision automation, and data insight generation Evaluate and integrate open-source and commercial AI frameworks into orchestration workflows Ensure AI solutions are explainable, auditable, and compliant with public- sector governance standards Advise on AI strategy … Python and popular AI/ML frameworks (e.g., PyTorch, TensorFlow, LangChain, Hugging Face) Familiarity with LLM fine-tuning, reasoning, and retrieval-augmented generation (RAG) techniques Experience integrating AI models into production systems or orchestration platforms Strong analytical and problem-solving skills, with the ability to translate research into practical outcomes Excellent written and verbal … recruitment keywords: Artificial intelligence large language models AI machine learning Agentic AI multi-agent Python AI/ML PyTorch TensorFlow LangChain Hugging Face LLM retrieval augmented generation RAG AI models orchestration platforms Public sector SC Security Clearance agent architectures ReAct AutoGen CrewAI BabyAGI reasoning loops memory systems governance frameworks public-sector AI standards BPMN More ❯

and develop AI-driven systems and tools that enhance the learning experience for students and educators. Build and deploy LLM-based solutions, integrating frameworks such as LangChain, AutoGen, and RAG pipelines. Develop and optimise AI agents capable of reasoning, planning, and automating complex learning or data workflows. Collaborate with cross-functional teams to integrate AI components into scalable production systems. … and product teams to ensure AI models are reliable, efficient, and explainable. AI ENGINEER - ESSENTIAL SKILLS: Proven experience working with LLMs and agentic AI frameworks (e.g. LangChain, AutoGen, CrewAI, RAG). Strong proficiency in Python for AI development, data processing, and API integration. Solid understanding of AI system design, prompt engineering, and model fine-tuning. Experience working with SQL and … HAVE: Experience integrating AI models into production systems using GCP, AWS, or Azure. Familiarity with vector databases, embedding models, or retrieval-augmented generation (RAG). Knowledge of Docker, Airflow, or MLOps pipelines. Strong understanding of AI ethics, data privacy, and responsible model deployment. TO BE CONSIDERED... Please either apply online or email me directly More ❯

production-grade LLM applications on Azure. You’ll work on AI-enabled and augmented intelligence solutions such as retrieval-augmented generation (RAG), agentic workflows, and model integrations with a strong emphasis on reliability, performance, security, and continuous improvement. Main Duties and Responsibilities Data & Retrieval Build robust ingestion pipelines for PDFs …/Word/Excel/Audio/JSON and semi-structured sources. Design RAG systems : chunking strategies, document schemas, metadata, hybrid/dense retrieval, re-ranking, and grounding. Manage vector/keyword indexes (e.g., Azure AI Search , pgvector, Pinecone/Weaviate). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and … Vault , private networking. Add observability : tracing/telemetry (OpenTelemetry, LangSmith), metrics, logs, cost and token usage monitoring, alerts. Apply evaluation & QA : regression suites, offline eval sets/golden data, RAG evals (faithfulness, answer relevance, citation correctness), A/B tests, win-rate testing. Ensure reliability : rate-limit handling, retries/backoff, idempotency, circuit breakers, caching (e.g., Redis/semantic cache More ❯

as advanced chatbots, summarization tools, document Q&A systems, and code assistants. Advanced Techniques: Apply sophisticated techniques like Prompt Engineering , Retrieval-Augmented Generation (RAG) , and context-aware pipelines to maximize model accuracy and relevance. Integration: Seamlessly integrate deployed AI models with existing enterprise systems, APIs, and data stores using back-end languages like Python … Data Engineering for Generative AI: Experience in preparing and curating diverse training datasets (structured/unstructured text, images, code). Deep knowledge of data preprocessing, tokenization, and embedding generation techniques. Hands-on experience working with Vector Databases (e.g., Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Stakeholder & Strategic Partnership: Ability to partner effectively with business More ❯

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

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

to £90,000 (DOE) + performance bonus Permanent Hybrid UK nationals only | Must hold SC clearance About the Role An innovative UK technology company is developing a next-generation AI platform designed for defence and health applications. Were looking for a Software Engineer who thrives on learning quickly, solving complex technical problems, and building systems that make a … driven business. What Youll Work On Design, build, and scale intelligent systems and microservices Develop AI-driven capabilities (e.g. LLMs, retrieval-augmented generation (RAG), and AI agents) Build and maintain APIs, data pipelines, and backend components (primarily Python, FastAPI, or Flask) Engineer robust, containerised architectures using Docker and Kubernetes Work with ElasticSearch, Weaviate, Pinecone 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 ❯

for managing context and tool interfaces for agents LLM integration patterns including prompt orchestration, multi-agent planning and tool calling Retrieval-Augmented Generation (RAG) for dynamic context injection Model selection/A/B testing/observability What Will Success Look Like Delivers high-quality, maintainable code that meets business needs Takes ownership of More ❯

Mastery: Strong skills in Python, JavaScript, or similar languages. LLM & Agent Expertise: Hands-on experience with LLMs (OpenAI, Anthropic, Mistral) and agent frameworks. Advanced Tools: Familiarity with vector databases, RAG pipelines, prompt engineering, REST APIs, cloud deployment, and containerization (Docker/Kubernetes). Domain Knowledge: Experience in one or more business domains such as finance, housing, operations, or customer service. More ❯

AI Engineers x 2 + fully remote contract + initially 6 months + £500 to £525 per day - Inside IR35 Key Skills: + Design of Gen AI Models + RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use … cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. + Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or … . Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯

AI Engineers x 2 + fully remote contract + initially 6 months + £500 to £530 per day - Inside IR35 Key Skills: + Design of Gen AI Models + RAG + AI/ML Pipelines The Role: + Design, prototype, and deploy Generative AI models (LLMs, Transformers, Diffusion models) for enterprise use … cases. + Build and fine-tune LLM-based applications (chatbots, summarization, document Q&A, report generation, code assistants, etc.). + Apply prompt engineering, RAG (Retrieval-Augmented Generation), and context-aware pipelines to ensure accuracy and relevance. + Integrate AI models with enterprise systems, APIs, and data stores using Python, Java, or … . Ensure compliance with AI ethics, security, and governance standards. Prepare and curate training datasets (structured/unstructured text, images, code). Apply data preprocessing, tokenization, and embedding generation techniques. Work with vector databases (Pinecone, Weaviate, FAISS, Chroma) for semantic retrieval use cases. Partner with business stakeholders to identify and shape AI use cases. Contribute to More ❯

leading a small, high-performing team. What You’ll Do Build and maintain data pipelines and cloud infrastructure (AWS/GCP). Develop AI-driven applications including LLM tools, RAG pipelines, and automation solutions. Lead on client communication , project delivery, and stakeholder alignment. Collaborate with Data Science and Analytics teams to improve data access, automation, and governance. Drive innovation through More ❯

and Generative AI R&D. Strong understanding of machine learning, data modelling , and natural language processing (NLP) techniques. Experience with fine-tuning , prompt orchestration , and retrieval methods (RAG, knowledge graphs). Familiarity with frameworks such as LangGraph , Azure AI Foundry Agents , and Semantic Kernel Agents . Good understanding of software engineering best practices , including version control, testing, and More ❯

experience building and deploying machine learning models in a production environment. Strong programming skills and deep expertise in Python. Hands-on experience building with agentic or RAG (Retrieval-Augmented Generation) frameworks like LangChain or LlamaIndex. Familiarity with tools for working with Large Language Models via API or in a local context (e.g. HuggingFace transformers More ❯

with Google Cloud Run and Kubernetes (GKE) . Familiarity with GCP data tools like BigQuery and Google Cloud Storage (GCS) . Experience building complex agentic AI applications (beyond basic RAG or LangChain implementations). How We Evaluate Candidates Our interview process is designed to identify practical engineering competence and collaborative skills. Technical Acumen: The technical assessment is rigorous. We evaluate More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯

Generative AI solutions using Azure AI Foundry, Azure OpenAI and Azure ML. Collaborate with clients to define use cases and translate business needs into technical solutions. Building and implementing RAG pipelines and fine-tuning models. Presenting technical concepts to both technical and non-technical stakeholders. Contributing to agile delivery processes and sharing knowledge through workshops and demos. To be successful More ❯