Hire Rahul R. - LLM Engineer

* Thesis: Towards Small Language Models for Security Query Generation in SOC Workflows * Designed a two-stage SLM-LLM architecture for Kusto Query Language (KQL) generation, reducing estimated token costs by 15x compared to state-of-the-art methods while achieving 97% syntactic correctness. * Performed Supervised and Chain-of-Thought (CoT) Fine-Tuning of SLMs using Performance-Efficient Fine Tuning (PEFT) and Low-Rank Adaptation (LoRA), achieving KQL syntactic correctness up to 85%. * Replicated and benchmarked LLM-based data engineering pipelines to quantify the effectiveness of published, state-of-the-art methodologies. * Developed evaluation metrics to assess the semantic validity and accuracy of LLM-generated KQL queries.