Enterprises are widely struggling to transition from AI prototypes to reliable production systems. While building initial demos is easy, operating LLM systems at scale introduces unique operational challenges—such as unpredictable inference costs, silent failures like hallucinations, and prompt drift—that traditional software engineering and basic prompt engineering cannot solve.
To bridge this skills gap, organizations need production-ready engineers who bring systems-engineering discipline, robust evaluation infrastructure, and cost-discipline rather than just model trivia. Traditional resume-matching staff augmentation often fails to source this talent, making specialized partners like Ardan Labs essential for deploying AI infrastructure that remains trustworthy under real-world pressure.