AI Research & Data Intelligence Engineer

• 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.

• 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.

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