6 of 6 Semantic Search Jobs in the North of England

techniques (e.g. CatBoost, XGBoost, RoBERTa), and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker More ❯

techniques (e.g. CatBoost, XGBoost, RoBERTa), and leveraging both structured and unstructured data sources. Experimenting with the integration and fine-tuning of models with Vector databases and embeddings to support semantic search, RAG (retrieval-augmented generation), and domain-specific applications. Working within a Data Mesh architecture, collaborating across domains to ensure scalable, interoperable data products; containerising solutions with Docker 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 ❯

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 ❯

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 ❯

PubSec) Elastic Manchester, England, United Kingdom 8 hours ago Be among the first 25 applicants Join to apply for the Principal Solutions Architect - (PubSec) role at Elastic Elastic, the Search AI Company, enables everyone to find the answers they need in real time, using all their data, at scale — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and … focused on excellence; taking the initiative to improve both themselves and the team through continuous learning and questioning the status quo. What Will You Be Doing Architect next-generation search solutions: Design and implement cutting-edge solutions utilizing Elastic's latest advancements in AI, semantic search, and vector search, including RAG, vector databases, and other emerging More ❯