Prakhar Mangal
Generative Ai Engineer @Readyly
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WORK HISTORY
Generative Ai Engineer @Readyly
Jaipur, IN
Architected and deployed a scalable multi-agent AI network, enabling collaborative reasoning, tool usage, retrieval, and autonomous task execution across enterprise workflows.Designed and implemented RAG-based workflow automation systems, integrating LLMs with enterprise knowledge bases, structured databases, and APIs to automate complex, multi-step business processes.Built and fine-tuned multiple ML/DL models to enhance conversational intelligence, domain adaptation, and contextual reasoning in production-grade AI systems.Developed and productionized LLM-powered chatbots and AI agents with a focus on latency optimization, reliability, and scalable deployment for government and commercial clients.Translated applied GenAI research into robust, real-world AI systems driving intelligent automation and operational efficiency at scale.
EDUCATION
Disha Delphi Global Sr. Sec. School, Kota
Secondary
Indian Institute of Technology, Delhi
Bachelor of Technology, Computer Science
Bhagat Public School, Kota
Senior Secondary
Noble Public School, Hindaun City, Rajasthan, India
8th Standard
Modern Public School, Kota
9th Standard
ABOUT PRAKHAR MANGAL
IIT Delhi (CSE) graduate, AIR-95 (JEE Advanced), building advanced AI systems at the intersection of Generative AI, large-scale machine learning, and autonomous agent architectures. I specialize in designing and deploying production-grade multi-agent AI systems that reason, retrieve, decide, and execute across complex enterprise environments. At Readyly, I architect scalable AI agent networks and RAG-based workflow automation frameworks that integrate LLMs with structured databases, APIs, and enterprise knowledge systems to automate high-impact, multi-step processes. My focus is not just model building — it is end-to-end AI system engineering: • Multi-agent orchestration & autonomous reasoning systems • Retrieval-Augmented Generation (RAG) at scale • LLM fine-tuning & domain adaptation • Latency-optimized, production AI deployments • ML/DL model training across vision and language domains I operate with a systems-first mindset — designing architectures that are scalable, fault-tolerant, and performance-driven. From deep learning model development to large-scale AI infrastructure, I build solutions that move from research-grade concepts to real-world execution. I am particularly driven by problems that require intelligence, autonomy, and precision at scale — building AI systems that don’t just respond, but reason and act.
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