7 of 7 Information Retrieval Jobs in England

is a hands-on engineering position focused on building and operating 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 More ❯

and help build the foundations of a generational product. 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 … complex ideas simple and actionable. Shipping fast, learning faster, and iterating on user and stakeholder feedback. We Are Looking For Someone Who: Has hands-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 More ❯

change and efficiency. Designs, builds, and deploys impactful AI and data-driven applications using cloud, data mesh, and knowledge base technologies such as centralized repositories, semantic search, and automated information retrieval systems that organize, store, and provide easy access to critical business data and insights. Integrates advanced analytics models and applications into operational workflows to ensure business value More ❯

the technical foundations for AI at Chambers, developing scalable solutions that push our technology forward and transform how we deliver value to 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). Develop and deploy advanced NLP, information retrieval, and recommendation systems that enhance Chambers and Partners’ research and product offerings, including document understanding, automatic summarisation, topic modelling, semantic search, entity recognition, and relationship extraction. Design and implement intelligent tagging … knowledge graphs and tool routing. Cloud deployment & MLOps Production deployments on Azure (AKS/ACI/Functions), CI/CD, and Infrastructure as Code (Bicep/Terraform). Data & Information Management Experience with relational/semi-structured database (MS SQL and Cosmos DB) and vector search indexing (Azure AI Search/pgvec More ❯

for a Lead Machine Learning Engineer — a "plug-and-play" leader with proven experience in people management and deep technical expertise — to shape the direction of our GenAI and retrieval-focused streams. This is a leadership role where you’ll not only design and build advanced machine learning solutions but also line-manage engineers, coordinate delivery across high-impact … you'll be doing: Lead and manage a team of machine learning engineers — providing mentorship, setting performance standards, and guiding career development. Set the technical direction for GenAI and retrieval-based systems, ensuring consistent architecture, tooling, and research-to-production workflows. Act as delivery owner across multiple ML streams, coordinating timelines, resources, and cross-functional collaboration. Drive best practices … in machine learning engineering — with production-level experience in Python, SQL, and frameworks like PyTorch or scikit-learn. Deep expertise in GenAI, LLMs, RAG systems, NLP, recommendation engines, or information retrieval stacks. Strong understanding of the full model lifecycle — from research and experimentation to deployment and monitoring. Familiarity with cloud-based ML infrastructure — especially AWS. Clear communication skills More ❯

around sentiment analysis, data ingestion and synthetic audience creation. Essential Skills for the AI Engineer: - Must have experience in building Large Language Model based applications incorporating tool usage and information retrieval. - Must have experience in Python programming language. - Must have experience administering Amazon Web Services accounts. - Must be able to take ownership of the entire feature development lifecycle, i.e. … AI. Your expertise will help shape a product that delivers real impact for the public, making a tangible difference in the efficiency and effectiveness of government services. For further information, contact Andrew Medhurst at Inspire People. Inspire People is acting as an employment business to the end user and candidates. More ❯

to join our team. The ideal candidate will bring deep expertise in building and deploying AI/ML solutions, particularly in areas like NLP, document understanding, and search/retrieval systems. You will work in a dynamic environment, collaborating across teams to design scalable AI-driven solutions and bring them into production. Key Responsibilities: Design, develop, and deploy AI …/ML solutions with a focus on NLP, document understanding, and information retrieval. Integrate AI models into systems using APIs and message brokers such as REST and Kafka. Implement secure systems using modern authentication and authorization standards like OAuth2, JWT, and Azure AD. Apply best practices in AI model evaluation, tuning, and optimization. Work with LLM architectures and frameworks … OpenAI, Anthropic, LangChain, LlamaIndex) to build scalable and intelligent applications. Utilize vector databases (e.g., FAISS, Azure AI Search) and embedding models for semantic search and retrieval tasks. Execute and iterate on PoCs rapidly to evaluate feasibility and solution effectiveness. Collaborate with cross-functional teams and stakeholders to align AI solutions with business objectives. Must-Have Skills: 12+ years of More ❯