AI Research & Data Intelligence Engineer

Silicon Logic UK Ltd.; West of England; Hybrid; GBP 60,000 - 90,000; Full time

Job summary

Silicon Logic UK Ltd. is seeking an AI & Data Intelligence Engineer to advance retrieval, search and RAG-based AI capabilities within a large enterprise data environment.

The role blends hands-on prototyping with research, delivering measurable analytics solutions and conversational capabilities. The candidate will prototype LLM-based reasoning workflows, design evaluation frameworks, and collaborate across data, architecture and product teams to translate research into practical, scalable

Qualifications

• Strong experience with retrieval methods, search strategies and RAG architectures.
• LSLM architectures, reasoning techniques and agentic systems.
• Experience with evaluation frameworks, experimentation and statistical methods.
• Proficient in SQL and data analysis on GCP/BigQuery/Teradata.
• Experience with research methodologies and hypothesis-driven experimentation.
• Strong Python skills for AI prototyping and rapid experimentation.
• Ability to translate research insights into practical prototypes and recommendations.
• Excellent analytical thinking and stakeholder communication.
• Experience in complex enterprise data environments.
• Familiarity with semantic layers, ontologies, knowledge graphs and graph-based reasoning.

Responsibilities

• Research and evaluate retrieval methods, search strategies and RAG patterns for enterprise use cases.
• Design and prototype LLM-based reasoning workflows and agentic systems.
• Define evaluation frameworks, benchmarks and measurable success criteria.
• Conduct hypothesis-driven experimentation and apply statistical methods to interpret results.
• Perform advanced SQL-based data exploration across GCP, BigQuery, Teradata or equivalent platforms.
• Build Python AI prototypes and translate research concepts into demonstrable solutions.
• Review literature to identify relevant methods and emerging capabilities.
• Collaborate with data, architecture, product and engineering teams to shape scalable AI solutions.
• Document research findings, design decisions and risks, with actionable recommendations.
• Support Dialogue with Data capabilities and conversational analytics initiatives.

Skills

Retrieval methods

RAG architectures

LLM architectures

Evaluation frameworks

Statistical methods

SQL data analysis

BigQuery

Teradata

Python programming

Research methodology

Prototype development

Cross-team collaboration

Communication skills

Responsible AI

Enterprise data environments

NL2SQL

Text-to-SQL

Knowledge graphs

Tools

NL2SQL

Text-to-SQL

Knowledge graphs

Job description

• Location : Bristol
• Duration : 1 year
• Work Mode : 2 days per week work from office

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.
• 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.