Many enterprise systems including large-scale deployment platforms like Ansible provide a declarative user interface through programming languages like JavaScript Object Notation (JSON). These systems maintain integrity through validation rules, typically enforced via JSON schemas. However, enterprise tasks in these systems are often complex, involving multiple schemas, which makes it challenging for the developers to select the appropriate ones and write schema-compliant code snippets for each task. Recently, Large Language Models (LLMs) have shown promising performance for many declarative code generation tasks when adopted with constrained generation using a pre-known schema. However, to cater to real-world enterprise tasks, each task often requiring multiple code snippets to generate while ensuring compliance with their respective schemas, we introduce a novel framework that allows LLMs to generate multiple code snippets while choosing an appropriate schema for each of the snippets for constrained generation. To the best of our knowledge, we are the first to study this crucial enterprise problem for declarative systems and preliminary results on two real-world use cases demonstrate substantial improvements in both syntactic and semantic task performance. These findings highlight the potential of the approach to enhance the reliability and scalability of LLMs in declarative enterprise systems, indicating a promising direction for future research and development.