Panel discussion - From Prompt to Production: Operationalizing Agentic LLM Systems
-- LLM agents, agentic systems, operationalization, AI deployment, orchestration, scalability, monitoring, AI ethics, real-world applications, generative AI--
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Synopsis:
As large language models (LLMs) evolve from static, prompt-based tools into autonomous, agentic systems capable of reasoning, planning, and acting with minimal human oversight, organizations face an exciting yet complex frontier. These advanced systems hold the potential to revolutionize enterprise workflows, developer tools, and customer-facing applications—but realizing that potential requires navigating a host of technical and ethical challenges.
This panel brings together leading voices from AI research, infrastructure engineering, and real-world application domains to discuss how agentic LLM systems are moving from lab experiments to production-grade deployments. Panelists will explore critical topics such as orchestration, safety, observability, and evaluation, while offering hard-earned lessons from deploying these systems at scale.
Whether you're building tools for developers, integrating LLM agents into enterprise pipelines, or shaping the next wave of intelligent products, this discussion will equip you with the strategic and technical know-how to bring agentic AI into impactful, everyday use. Don’t miss this opportunity to learn what it truly takes to operationalize the future of AI.
By registering for this event, you agree that IEEE and the organizers are not liable to you for any loss, damage, injury, or any incidental, indirect, special, consequential, or economic loss or damage (including loss of opportunity, exemplary or punitive damages). The event will be recorded and will be made available for public viewing.
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- Starts 29 July 2025 07:00 AM UTC
- Ends 17 September 2025 07:00 AM UTC
- No Admission Charge
Speakers
Yubin Kim
Panelist
Biography:
Dr. Yubin Kim is the Co-Founder and Chief Science Officer at Vody, a generative AI startup focused on transforming e-commerce discovery using multimodal large language models. At Vody, she leads scientific strategy and product development, building systems that enhance product search, recommendations, and catalog understanding by combining structured data with visual and textual signals.
Yubin holds a Ph.D. in Language Technologies from Carnegie Mellon University, where her research focused on information retrieval, distributed selective search, and retrieval system evaluation. Before founding Vody, Dr. Kim held key applied machine learning roles in industry, including leading search and recommendation teams at Etsy, where she worked on improving buyer experience through personalized discovery systems. She has a track record of translating rigorous research into impactful, user-facing products.
In addition to her industry work, Yubin is active in the research community. She has served as a program committee member, organizer, and reviewer for top conferences such as SIGIR, CIKM, and ICTIR, and is a frequent speaker at workshops and forums bridging academia and industry.
Gautam Solaimalai
Panelist
Biography:
Gautam Solaimalai is a Vice President and Senior Software Engineering Manager at U.S. Bancorp, specializing in cloud-native enterprise architecture, AI-driven automation, and financial technology innovation. With over 15 years of experience across industry leaders like Honeywell, OneTrust, and U.S. Bank, Gautam has led strategic product development and digital transformation initiatives that power secure, scalable financial platforms. He is a published author in IEEE journals, a peer reviewer for global tech conferences, and a recipient of multiple innovation and excellence awards. Gautam is also an active contributor to the technology community through mentoring, technical leadership, and applied research in AI, IoT, and DevOps.
Shaleen Kumar Gupta
Panelist
Biography:
Shaleen Kumar Gupta is a Software Engineer in the Machine Learning team at Google Search, where he works on the Generative Search Experience, building LLM-powered systems that provide grounded, multimodal answers to user queries. His work spans LLM agent design, post-training for response quality, retrieval and ranking across search verticals, and multimodal fusion for shopping and short video search.
Shaleen earned his Master’s degree in Computer Science from the Language Technologies Institute at Carnegie Mellon University (CMU), where his research focused on deep learning, natural language processing, and multimodal AI. His MS capstone, “Hinglish Code-Mixed Conversational Agents,” was advised by Prof. Alan Black. During his time at CMU, he also served as a teaching assistant for graduate courses in Artificial Intelligence and Cloud Computing.
Prior to Google, he gained diverse experience across AI and software engineering roles at companies including Directi, Morgan Stanley, and as a research assistant at Nanyang Technological University (Singapore). His early career includes contributions to high-performance computing, natural language processing, and black-box optimization research.
Shaleen has a strong foundation in applied machine learning, with contributions ranging from scalable ML systems at Google to knowledge extraction pipelines and conversational agents in academic and startup settings.
Vishal Jain
Panelist
Biography:
Vishal Jain is a Staff software engineer at Meta and works on building scalable digital solutions. With extensive experience in quantitative finance and technology leadership roles at Meta, Two Sigma, and Bloomberg, he brings expertise in developing innovative solutions across industries. Vishal’s strong foundation in engineering and data-driven problem-solving, supported by his education at IIT Kanpur and Columbia University, positions him as a forward-thinking leader in the tech space.
Rahul Raja
Moderator
Biography:
Rahul is a Staff Engineer at LinkedIn, with over 10 years of experience in NLP, LLMs, MLOps, and scalable AI systems. He is a CS graduate from Carnegie Mellon University, USA and working as a Staff engineer at Linkedin in the Information Retrieval (IR) team, focusing on cutting-edge challenges in ML infrastructure, ranking algorithms, and machine learning.His work spans Generative AI, recommender systems, and multimodal ML, with contributions to both industry and peer-reviewed research.
Harsh Varshney
Moderator
Biography:
Harsh Varshney is a Software Engineer at Google, working on the Privacy Sandbox to develop privacy-preserving web technologies. He brings deep experience in distributed systems and infrastructure, with past roles at Splunk, AWS, Flipkart, and ClearTax, where he built large-scale backend systems across data and fintech domains.
Harsh holds a Master’s degree in Computer Science from Carnegie Mellon University, executive training from Stanford GSB, and dual degrees from BITS Pilani. He is passionate about building scalable, reliable systems that power impactful real-world applications.
