> Markdown version of [/jobs/ext/3710964-ai-research-data-intelligence-engineer](https://www.wearedevelopers.com/jobs/ext/3710964-ai-research-data-intelligence-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Research & Data Intelligence Engineer - **Company:** LA International Computer Consultants - **Location:** Bristol, UK - **Salary:** £117,000.0 - £143,000.0 - **Contract:** Temporary contract - **Skills:** Artificial Intelligence, Data Analysis, BigQuery, Data Architecture, Data Systems, Graph Database, Data Intelligence, Python (Programming Language), SQL Databases, Teradata SQL, Google Cloud, Sql Optimization, Retrieval-Augmented Generation, Large Language Models, Agentic-AI, Data Layers, Knowledge Representation, Data Management - **Published:** October 13, 2026 - **Apply:** https://www.careerboard.com/gb/en/find-jobs-in-United-Kingdom/-ABC2CFBC636A76DC0D/ ## About the Role * Strong experience with retrieval methods, search strategies, and RAG architectures. * Good understanding of LLM architectures, reasoning techniques, and agentic systems. * Experience with evaluation frameworks, experimentation, and statistical methods. * Strong hands-on SQL and data analysis skills using GCP, BigQuery, Teradata, or equivalent platforms. * Experience applying research methodologies, conducting literature reviews, and using hypothesis-driven experimentation. * Strong Python skills for AI prototyping, rapid experimentation, and proof-of-concept development. * Ability to consume, interpret, and apply relevant academic and industry research. * Strong analytical, problem-solving, documentation, and stakeholder communication skills., * Retrieval methods, search strategies, and RAG architectures. * LLM architectures, reasoning techniques, and agentic systems. * Evaluation frameworks, experimentation, and statistical methods. * Strong hands-on SQL and data analysis skills using GCP, BigQuery, Teradata, or equivalent technologies. * Research methodology, literature review, and hypothesis-driven experimentation; comfortable consuming research. * Python programming and AI prototyping skills. * Ability to convert research insights into practical prototypes, experiments, and recommendations. * Strong analytical thinking and structured problem-solving skills. * Ability to collaborate effectively across AI, data, architecture, and product teams. * Clear written and verbal communication skills, with the ability to explain complex technical concepts. * Awareness of responsible AI, model limitations, evaluation risks, and enterprise governance considerations. * Experience working in complex enterprise data environments. Desirable skills/knowledge/experience: At least some exposure to the following is expected: * Semantic layers and metrics modelling; ontologies and knowledge representation; knowledge graphs and graph-based reasoning concepts. * Taxonomy and metadata design; NL2SQL, Text-to-SQL, broader NL2* methods, conversational analytics and Dialogue with Data (DwD) approaches; LBG data architecture and data product concepts. ## Description * Research and evaluate retrieval methods, search strategies, and RAG architecture patterns for enterprise use cases. * Design and prototype LLM-based reasoning workflows and agentic systems. * Define evaluation frameworks, benchmarks, experiments, and measurable success criteria for AI solutions. * Conduct hypothesis-driven experimentation and apply statistical methods to interpret results. * Perform advanced SQL-based data exploration and analysis across GCP, BigQuery, Teradata, or equivalent platforms. * Build Python-based AI prototypes and translate research concepts into demonstrable solutions. * Review academic and industry literature to identify relevant methods, patterns, and emerging capabilities. * Collaborate with data, architecture, product, and engineering teams to shape scalable AI and data solutions. * Document research findings, design decisions, evaluation results, risks, and recommendations. * Support the development of conversational analytics and Dialogue with Data capabilities.