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AI Research & Data Intelligence Engineer

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Onsite

AI Research & Data Intelligence Engineer

Silicon Logic UK Ltd.

AI Research & Data Intelligence Engineer

Skills

Google Cloud PlatformSQL QueriesPythonData AnalysisAI

13 days ago

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Important information


Contract type:

Freelance / Contract

Location:

Bristol, UK

Work mode:

Onsite

Published on:

12 October 2026

What they need


  • Role : AI Research & Data Intelligence Engineer
  • Location : Bristol
  • Duration : 1 year
  • Work Mode : 2 days per week work from office
  • Pay : 500 GBP per day (Inside IR 35)

The Role:

We are looking for a highly skilled AI & Data Intelligence Engineer with strong expertise in retrieval, search, Retrieval-Augmented Generation (RAG), large language models, evaluation, experimentation, and data analysis. The role will contribute to the research, prototyping, evaluation, and delivery of AI-powered data and conversational analytics capabilities.

The ideal candidate combines hands-on engineering and analytical skills with a research-oriented mindset, and is comfortable translating emerging research into practical, measurable solutions within a complex enterprise data environment.

Required Experience:

  • 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.

Your responsibilities:

  • 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.

Your Profile

Essential skills/knowledge/experience:

  • 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:

  • 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.

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