22 of 22 Contract Retrieval-Augmented Generation Jobs in England

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 ❯

by over 750 civil servants daily. Assist enables government communicators to brainstorm, draft, and review content using Claude (Anthropic) via Amazon Bedrock, enhanced with Retrieval Augmented Generation (RAG), file upload capabilities, and role-specific prompts. This is a unique opportunity to contribute to a business-critical tool with a bold roadmap for FY25 … User Researchers, and Behavioural Scientists to co-design and test product updates. . Contribute to technical decision-making, including Generative AI features such as Retrieval Augmented Generation (RAG), prompt engineering, and application design. . Design and develop new features, including sentiment analysis, data ingestion, and synthetic audience creation, while supporting Agile delivery through sprint More ❯

experience with cloud platforms (AWS, GCP, Azure) and MLOps workflows. Solid understanding of vector databases (e.g. Pinecone, Weaviate, Milvus) and retrieval-augmented generation (RAG) techniques. Comfort with rapid technical recall for industry benchmarks, model parameters, and framework capabilities. 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 ❯

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 ❯

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 ❯

experience in AI/ML and data engineering, with at least 3 years in a technical leadership role. Proven experience working with GenAI technologies (e.g., OpenAI, Hugging Face, LangChain, RAG pipelines). Strong programming skills in Python, SQL, and familiarity with cloud platforms (Azure, AWS, GCP). Expertise in data architecture, ETL/ELT pipelines, and distributed computing frameworks (e.g. 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 ❯

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. Please click here to find out more about our Key Information Documents. Please note that the documents provided contain generic information. If we are successful in finding you an 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 ❯

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 ❯

likely to extend this is paying £475 a day In order to be considered for this role you will need to have - LLM applications: LangChain, Pydantic-AI (similar frameworks), RAG systems, MCP servers Front end: some experience with Typescript, REACT Application servers, web servers, database servers, docker Some context: frontend (TS/React) and a backend (Python/FastAPI) leveraging 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 ❯

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 ❯

support secure, air-gapped AI deployments, with a focus on NLP-based tooling * Develop pipelines for transcription ingestion and real-time analytical insight generation * Support graph and RAG-based inference layers using data from structured and unstructured sources * Build and expose APIs for frontend consumption, enabling natural-language querying, dynamic visualisation, and reporting * Ensure system portability via containerisation More ❯

support secure, air-gapped AI deployments, with a focus on NLP-based tooling * Develop pipelines for transcription ingestion and real-time analytical insight generation * Support graph and RAG-based inference layers using data from structured and unstructured sources * Build and expose APIs for frontend consumption, enabling natural-language querying, dynamic visualisation, and reporting * Ensure system portability via containerisation … clear grasp of the constraints and workarounds involved * Comfort designing APIs for real-time and batch AI/ML pipelines. * Experience supporting or building graph-based systems (e.g., for RAG or knowledge graph traversal) is a plus Experience working with LLMs, vector stores, or transcription pipelines Experience in Defence, aerospace, or other secure environments Due to the nature and urgency More ❯

Doing: Architecting and implementing secure backend services to support AI deployments in classified settings Developing transcription ingestion pipelines and real-time insight generation capabilities Supporting graph and RAG-based inference pipelines using structured and unstructured data Exposing robust APIs for frontend engineers to deliver interactive dashboards and NLP-driven workflows Ensuring system portability via containerisation (Kubernetes, Docker) Collaborating … of Postgres or similar databases Proven experience building real-time or batch AI/ML pipelines with strong API design (Bonus) Exposure to LLMs, vector databases, transcription pipelines, or RAG systems (Bonus) Prior work in Defence, aerospace, or national security environments Why This Project? This isn’t another generic SaaS deployment. You’ll be building backend systems that enable critical More ❯

part of a small, elite, cross-functional team. What You’ll Be Doing: Designing and implementing NLP pipelines for transcription, topic extraction, and summarisation Developing modular LLM workflows (e.g., RAG, graph-based reasoning) optimised for low-resource deployments Integrating structured and unstructured data: transcripts, policy documents, supply chain models Collaborating with platform engineers to containerise models for edge inference Defining … experience building and deploying NLP/LLM solutions, including summarisation, entity extraction, and topic modelling Strong Python skills with libraries such as HuggingFace Transformers, spaCy, PyTorch/TensorFlow, or RAG frameworks Experience with knowledge graphs, GNNs, or graph-enabled retrieval Proven ability to optimise ML systems for air-gapped, low-compute, or edge environments Understanding of evaluation methods More ❯

high-performance services using Golang in secure environments Develop ingestion and processing pipelines for transcription and structured/unstructured data Contribute to intelligent data flows including graph-based and RAG inference pipelines Expose well-structured APIs to support real-time dashboards and NLP workflows Deploy and scale systems using Kubernetes and Docker Collaborate with platform and data science teams to … orchestration tooling (e.g. Argo), and Kafka or equivalent Strong database knowledge (Postgres preferred) Ability to design and scale APIs and backend pipelines for data-rich environments Bonus: Familiarity with RAG pipelines, transcription tooling, vector DBs, or LLM deployment Bonus: Prior experience within Defence, aerospace, or highly regulated industries Why It Stands Out: This isnt a generic software build. Youll help More ❯

high-performance services using Golang in secure environments Develop ingestion and processing pipelines for transcription and structured/unstructured data Contribute to intelligent data flows including graph-based and RAG inference pipelines Expose well-structured APIs to support real-time dashboards and NLP workflows Deploy and scale systems using Kubernetes and Docker Collaborate with platform and data science teams to … orchestration tooling (e.g. Argo), and Kafka or equivalent Strong database knowledge (Postgres preferred) Ability to design and scale APIs and backend pipelines for data-rich environments Bonus: Familiarity with RAG pipelines, transcription tooling, vector DBs, or LLM deployment Bonus: Prior experience within Defence, aerospace, or highly regulated industries Why It Stands Out: This isn’t a generic software build. You More ❯

support secure, air-gapped AI deployments, with a focus on NLP-based tooling Develop pipelines for transcription ingestion and real-time analytical insight generation Support graph and RAG-based inference layers using data from structured and unstructured sources Build and expose APIs for frontend consumption, enabling natural-language querying, dynam click apply for full job details More ❯