Lohitaksh Yogi

About Lohitaksh Yogi

I am a Product leader driving the strategic vision and customer experience in both B2B and B2C for Multi Modal assistant, Generative AI Assistant, Generative AI, Conversational AI, Search, Recommendation, and AI Learning Platforms. With a strong background in product management, Agentic AI, generative AI, multimodal and multilingual LLM, deep learning, and machine learning, I am well-equipped to lead innovative projects.My mission is to leverage generative AI to create solutions that automate and enhance customer interactions, increase revenue, improve retention, and boost customer satisfaction. I have successfully launched and managed SaaS products using state-of-the-art technologies like LLM, GPT, Transformer, multimodal, NLU, and NLG. I have also collaborated with stakeholders, conducted market research, and built cross-functional teams to deliver high-quality AI products in under a year.

Experience

1. Technical Principal Product Manager Platform (Multimodal, Agentic & Generative Ai)

   ServiceNow

   Mar 2025 — Present · Santa Clara, CA, US

   As a Principal Technical Product Manager at ServiceNow, I drove the evolution of enterprise AI by building an agentic, multimodal, multilingual Virtual Assistant—a true digital teammate powered by LLMs, LMMs, and agent communication protocols. Deployed across ITSM, HRSD, CSM, and Procurement, it reached 200+ enterprise customers, delivered a 35% increase in self‑service resolution, 40% AHT reduction,$12M+ in customer savings, and 3× subscription growth, supported by tiered pricing, a custom agent marketplace, multichannel/multilingual support, and partner white‑labeling.Led AI-native product discovery by rapidly prototyping conversational and multimodal experiences using Figma Make, Claude prototyping, and vibe-coding tools like Replit and Lovable, supported by AI coding copilots (Cursor, Windsurf, GitHub Copilot) to turn concepts into clickable and functional demos quicklyI led the architecture of a platform that could autonomously plan, execute, and collaborate across systems. We integrated, Applied Agentic AI strategies—including A2A orchestration and Model Context Protocol (MCP)—to launch a modular assistant with dynamic LLM orchestration, inter-agent collaboration, multimodal understanding, and instruction & reward tuning for continuous improvement.LLMs (GPT, Bard, LLaMA) for natural language understanding, LMMs/VLMs/SAMs for multimodal input processing, LAMs for task execution, MoE for scalable inference, SLMs/LCMs for edge deploymentsAgent Communication Protocols (MCP, A2A, ANP) for agent collaboration, MLMs for deep contextual understanding, Innovation Through Research.Fine-tuning (SFT, RLHF, DPO) for enterprise alignment, Few/zero-shot learning for adaptability, Reward modeling for behavior optimization, Model compression for edge performance, Multimodal fusion for contextual insights Platform to Product.

Education

• Stanford University Graduate School of Business

  Product Management

• University of North Carolina

  Master's degree, Computer Science

• Shri Dharmasthala Manjunatheshwara college of engineering and technology,dharwad

  Bachelor's degree, Computer Science