This hybrid engagement supports the development and production hardening of Natural Language Query (NLQ) systems, AI agents, and related applications powering talk-to-data experiences. The work focuses on improving NLQ accuracy and semantic understanding while building robust, scalable, and observable AI systems suitable for enterprise production environments.

This engagement requires senior-level software engineering depth, combined with AI evaluation and semantic modeling expertise, to translate research concepts into reliable, deployable systems.

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