INAYAT RAHIM

Specialized in Reinforcement Learning, AI Agents, RAG, and Multi-Agent Systems with research focused on maximizing ROI and business value through strategic AI implementation. Developed expertise in solving real-time enterprise challenges by optimizing computational efficiency and reducing operational costs. Research demonstrates measurable impact in overcoming time and resource constraints through innovative algorithmic strategies and scalable architectures.

Exploring advanced Prompt Engineering methods for LLMs with a focus on efficiency, reasoning, and adaptability. Designed strategies that accelerate business growth by unlocking new AI-driven capabilities, improving ROI through automation, and enabling companies to scale with data-driven insights. Actively solving real-time business problems in productivity, communication, and intelligent systems integration.

Leading groundbreaking research in Self-Supervised Fine-Tuning (SSFT) of transformer-based models for enterprise adoption. Dual focus on computational efficiency and business value creation through strategic model optimization. Developing frameworks that deliver measurable outcomes: optimized workflows, reduced operational costs, and enhanced ROI. Addressing critical industry challenges in AI deployment across healthcare, finance, and manufacturing sectors.