Khushi Malik

CGPA: 8.27/10.0

• Spearheaded optimization of Retrieval-Augmented Generation (RAG) pipelines, achieving an 18% increase in LLM accuracy and a 25% reduction in latency through advanced re-ranking and semantic chunking strategies.
• Led a cross-functional team of 5 engineers to ship an AI-powered VS Code extension utilizing multi-agent workflows, driving a 30% boost in developer productivity.
• Engineered cost-efficient model fine-tuning workflows using Hugging Face Transformers and PEFT (LoRA), reducing cloud infrastructure costs by 40%.
• Collaborated with executive stakeholders to define AI strategy and presented data-driven insights that accelerated adoption of AI initiatives by 15%.

• Optimized LLM inference by implementing tokenization improvements and prompt compression, increasing speed by 20% and response relevance by 15%.
• Architected scalable end-to-end ML pipelines using Python and Hugging Face, enabling production rollout within 4 weeks handling 10k+ daily queries.
• Designed and deployed enterprise-grade RAG systems for efficient retrieval across organizational datasets.