AI Research & Data Intelligence Engineer
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: 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.
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: 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.