11 of 11 Semantic Search Jobs in the UK excluding London

APIs and microservices that handle real-time threat analysis at scale Implement computer vision systems for image and video analysis in OSINT investigations Build and optimize vector databases for semantic search across massive intelligence datasets Establish best practices for AI/ML model deployment, monitoring, and continuous improvement Mentor team members on AI engineering practices and drive technical More ❯

APIs and microservices that handle real-time threat analysis at scale Implement computer vision systems for image and video analysis in OSINT investigations Build and optimize vector databases for semantic search across massive intelligence datasets Establish best practices for AI/ML model deployment, monitoring, and continuous improvement Mentor team members on AI engineering practices and drive technical More ❯

Experience identifying AI use cases and translating them into business value Preferred Experience with LibROSA, Essentia, or other audio analysis libraries Knowledge of computational musicology and MIR Familiarity with semantic search, vector databases, and explainable AI Understanding of music licensing and rights management Background in retail tech or B2B SaaS is a plus What We Offer A unique More ❯

Experience identifying AI use cases and translating them into business value Preferred Experience with LibROSA, Essentia, or other audio analysis libraries Knowledge of computational musicology and MIR Familiarity with semantic search, vector databases, and explainable AI Understanding of music licensing and rights management Background in retail tech or B2B SaaS is a plus What We Offer A unique More ❯

languages. Strong SQL and data analytics skills. Familiarity with cloud platforms (AWS and Azure) for AI deployment. Knowledge of MLOps principles for scaling AI models. Understanding of knowledge graphs, semantic search, and vector databases. AI Ethics and Responsible AI Awareness of AI ethics, bias mitigation, and fairness in models. Understanding of GDPR and compliance frameworks for AI in More ❯

AI Engineer to join our AI engineering team. This role is central to designing, developing, and iterating on fast-paced prototypes that explore the latest in LLMs, autonomous agents, semantic search, and reasoning workflows. The ideal candidate is proficient in Python, experienced in building multi-step intelligent systems, and comfortable working across UI, APIs, cloud AI platforms, and … Autogen. Engineer and tune prompts to enhance the performance and reliability of generative tasks. Design RAG systems using vector databases like Pinecone, Chroma, and PosgreSQL for contextual retrieval. Incorporate semantic search and embedding strategies for more relevant and grounded LLM responses. Utilize Guardrails to implement applications that adhere to responsible AI guidelines. Optimize model performance for latency, throughput More ❯

technology research and development cycle from research and design to implementation and evaluation. A background in areas such as LLM based agents, knowledge graph, knowledge reasoning/inference and semantic search (text/image/video) is desired. Responsibilities as Knowledge Computing Researcher : Design and implement core modules in knowledge-augmented agent systems, including knowledge retrieval, memory modelling … structured and unstructured knowledge from multiple modalities. Architect solutions that deeply couple retrieval systems (RAG, KGs, databases) with agent planning, reasoning, and execution workflows. Work closely with LLM platforms, search infrastructure, and knowledge graph systems to build collaborative end-to-end agent solutions. Translate cutting-edge research into deployable systems across scenarios such as virtual assistants, intelligent QA, multi More ❯

would be working as part of a high calibre team of engineers and researchers to build intelligent agent systems powered by large language models, knowledge graphs, and multi-modal semantic search. This is a unique opportunity to apply cutting-edge research to real-world AI systems with meaningful impact across applications such as intelligent assistants, knowledge-based reasoning, and … from multiple modalities (text, image, video) into agent workflows Develop solutions coupling retrieval (KGs, RAG, databases) with planning, reasoning, and execution logic Collaborate with engineering teams on LLM platforms, search infrastructure, and agent systems Translate research into production-ready applications across AI development tools, QA systems, and assistant use-cases Contribute to core algorithm development and support product scaling … ACL, NeurIPS, ICML, EMNLP, ICLR, AAAI) Strong coding and software design skills Experience working across research and applied development in a collaborative environment Keywords: Knowledge Graphs/LLMs/Semantic Search/Knowledge Reasoning/NLP/Agent Systems/RAG/OWL/SPARQL/Transformers/Deep Learning/AI Assistants/QA Systems/Pytorch More ❯

LEAD AI ENGINEER LIVERPOOL 2 DAYS ON-SITE INSIDE IR35 A hands-on, innovation-driven role focused on rapidly prototyping advanced AI systems using LLMs, autonomous agents, and semantic search. The engineer will work across the full stack-from prompt engineering and memory management to UI and cloud deployment-using tools like LangChain, CrewAI, and vector databases. Core Responsibilities More ❯

workflows that leverage tool usage and chaining Experiment with, tune, and benchmark different LLMs (e.g., OpenAI, HuggingFace, LangChain) Automate model evaluation and integrate insights into the dev lifecycle Support semantic search, data extraction, and document analysis use cases Collaborate with legal engineers and product to build secure and compliant AI systems Contribute to architectural decisions, internal tooling, and … APIs and cloud infra is a plus Proven track record designing evaluation frameworks and tuning model performance Solid understanding of data privacy, system integrity, and scalable architecture Experience with semantic search, retrieval pipelines, or document-based AI systems is a bonus Nice to Have: Exposure to legal tech, compliance tools, or privacy-centric workflows Familiarity with ML frameworks More ❯

data pipelines, and MLOps practices. Experience with Azure services such as Azure Machine Learning, Azure Data Factory, Azure Synapse, and Azure Functions. Implementing modern retrieval techniques such as vector search, semantic search and Retrieval-Augmented Generation (RAG) using tools like Azure Cognitive Search, FAISS, Pinecone, or Weaviate. Familiarity with data governance, privacy, and ethical AI principles. More ❯