1 to 25 of 39 Retrieval-Augmented Generation Jobs in the South East

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 Explainable AI. Key Skills/Experience: Engineering Foundations: Strong proficiency in Python and robust software engineering principles, with experience operating as part of engineering teams. Generative AI & RAG: Proven experience working with LLMs and designing Retrieval-Augmented Generation (RAG) architectures, including knowledge of semantic search and vector retrieval. AICA Proficiency: Experience using More ❯

The Role We're looking for an AI Engineer with deep experience in applied sub-fields such as recommender systems, retrieval-augmented generation (RAG), agentic AI, or automated decision-making to drive the development of our core product: Dragonfly's tool stack recommender engine. This role sits at the intersection of data science, product … of our recommender system. Designing and implementing AI/ML systems that scale gracefully. Applying and iterating on LLMs and data-driven approaches to improve recommendations. Rapidly experimenting with RAG, search algorithms, and agentic architectures. Debugging complex model behaviours and improving reliability and observability. Staying on top of the latest research and applying it pragmatically. Collaborating cross-functionally to make … on experience with search and information retrieval, including BM25, hybrid search, and ranking metrics (nDCG, Reciprocal Rank, etc.). Understands recommender systems, from collaborative filtering to modern RAG pipelines. Knows how to design agentic LLM systems, balancing cost, latency, and performance. Is familiar with observability frameworks such as Langfuse, LangSmith, or OpenTelemetry. Has worked with knowledge graphs and 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 ❯

advanced prompt engineering expertise, from constructing detailed prompts and assistant logic to experimenting with system prompts, LLM fine-tuning, and Retrieval-Augmented Generation (RAG). Serve as a vital link between production, creative, and technical teams, fluent in all three realms and adept at translating ideas seamlessly. Technical Expertise Assess and influence creative tech … pipelines would be an added advantage. Familiarity with machine learning frameworks such as TensorFlow and PyTorch is a valuable asset, although not mandatory. A solid understanding of image generation models, including Stable Diffusion and ControlNet, is greatly appreciated. You bring at least five years of experience in the creative and technology sectors, having thrived in interactive studios, experiential More ❯

Partner with Data Scientists to operationalize experimental models, enabling scalability and generalizability across diverse business domains. Integrate emerging ML engineering techniques (e.g., LLM deployment, fine-tuning pipelines, vector databases, RAG systems) into enterprise-ready solutions. Own the design of foundational ML platforms and frameworks that serve as building blocks for downstream AI applications. Embed controls, governance, and auditability into ML More ❯

model context protocol capabilities. Implement microservices architectures and manage robust relational and non-relational databases. Ensure our AI features leverage Retrieval-Augmented Generation (RAG), Model Context Protocol, and other leading-edge techniques—with a strong focus on safe and ethical deployment. Stay current with the latest in cloud, platform, data & AI engineering practices to … ideas drive real impact. Room to grow—professionally and personally—with team members who love what they do. Competitive compensation, benefits, and flexible working arrangements. Build a next generation AI native consultancy who are seeking to disrupt the status quo when it comes to traditional, tired and tested consulting approaches. More ❯

YouTube's APIs. Familiarity with building AI-powered systems, including integrating LLM APIs (e.g., OpenAI, Gemini) and understanding architectures like Retrieval-Augmented Generation (RAG). Experience in the Entertainment, Media or Music industries. 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 ❯

sharing and upskilling Proven experience building LLM-powered applications in production environments Strong prompt engineering skills with demonstrated ability to optimise for various use cases A deep understanding of RAG architectures, including vector databases, embedding strategies, and retrieval optimisation Strong experience with evaluation and observability tools for AI systems Familiarity with Agentic frameworks such as OpenAI Agent SDK 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 ❯

across platforms. About the Role Lead the design, implementation, and fine-tuning of LLMs and generative AI solutions (OpenAI, Anthropic Claude, Gemini, open-source models). Build and optimize RAG pipelines , vector databases, and prompt engineering strategies. Develop AI-powered applications integrating LLMs with APIs, agents, and workflows. Deploy and monitor AI models on cloud platforms (Azure preferred; AWS/ 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 ❯

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 ❯

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 ❯

our users. 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). … 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 ❯

You will act as both a technical thought leader and a strategic partner, fostering a culture of rigorous experimentation, reproducibility, and responsible AI adoption while mentoring the next generation of data scientists. Responsibilities: Lead the design and execution of data science projects that solve complex business problems across critical workflows. Develop and apply advanced AI/ML methods … into analytical frameworks, ensuring results are actionable and aligned with strategic priorities. Drive the adoption of emerging analytical techniques and tools (e.g., generative AI, LLM-based analytics, simulation modeling, RAG for knowledge discovery). Collaborate with ML engineers to scale prototypes into production-ready systems, ensuring reliability, fairness, and generalizability. Design and maintain metrics and experiments (A/B testing More ❯