I am Ayush Gupta, a passionate and skilled Full Stack LLM Development Analyst specializing in AI, machine learning, and cloud technologies. Over the years, I have developed expertise in building scalable AI architectures, particularly focusing on multi-agent AI systems and Retrieval-Augmented Generation (RAG) frameworks. My work has consistently aimed at reducing manual oversight, improving accuracy, and enhancing operational workflows through innovative AI solutions.
My experience at Accenture has allowed me to architect complex AI systems, including Model Context Protocol (MCP) integrations and vector search pipelines, which have significantly improved enterprise AI capabilities. I have also managed cloud infrastructure on platforms like Google Cloud Platform (GCP), enabling zero-downtime deployments and scalable inference endpoints.
Prior to this, I interned at BBIOT Technologies, where I contributed to backend development, containerization, and automation for IoT sensor networks. This role helped me hone my skills in NodeJS, Docker, and Azure Functions, while optimizing Python APIs for edge devices.
I hold a B.Tech degree in Computer Science and Engineering with a specialization in Bioinformatics from Vellore Institute of Technology. I have earned multiple certifications in cloud engineering and generative AI from leading providers such as Google Cloud, AWS, Oracle, and Microsoft.
I am committed to continuous learning and have been recognized with awards for my performance and contributions to AI and cloud-native communities. I am eager to leverage my skills and experience to drive innovative AI solutions that create real business value and advance the field of artificial intelligence.
Courses: Data Structures, Analysis Of Algorithms, Computer Networking, Operating Systems, Database Management Systems
Developed and deployed multi-agent AI architecture automating complex decision flows reducing manual oversight by 50%. Built enterprise-grade RAG systems improving contextual accuracy and reducing hallucinations by 50%. Architected MCP server integrations reducing custom glue-code by 40%. Created novel chunk selection process increasing content analysis accuracy by 15% and reducing processing time by 20%. Implemented scalable vector search pipelines improving retrieval precision by 35%. Managed GCP Vertex AI workspaces and Cloud Run services enabling zero-downtime deployments and auto-scaling inference endpoints. Integrated AI agents into Indian telecom cloud ecosystem improving operational workflows by 25%.
Built 25+ NodeJS/Express APIs serving 10,000+ IoT sensors. Containerized services with Docker Compose and implemented Azure Function automations cutting code-to-deployment time by 30%. Optimized Python APIs with async request handling boosting integration reliability by 25% and reducing communication latency by nearly 50%.
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