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Sitaram Iyer
ex Distinguished Engineer at Google
Professional Background
Sitaram Iyer is an accomplished software engineer with a strong foundation in computer science and engineering principles. With a remarkable educational background that includes a Doctor of Philosophy from Rice University and a Bachelor's degree from the prestigious Indian Institute of Technology, Bombay, Sitaram has established himself as an expert in the field of technology. Currently, he is contributing his talents to Google as a software engineer, where he applies his extensive knowledge to develop innovative solutions and streamline processes that enhance user experience.
Having joined Google, Sitaram has been instrumental in various projects that leverage cutting-edge technology and best practices in software development. His role encompasses a broad spectrum of responsibilities, including the design, development, testing, and implementation of software systems that are not only robust but also efficient.
Education and Achievements
Sitaram's educational journey reflects his commitment to excellence and continuous learning. At the Indian Institute of Technology, Bombay, he honed his skills in computer science and engineering, laying a solid groundwork for his advanced studies. The rigorous curriculum and hands-on projects at IIT Bombay equipped him with the analytical and problem-solving skills essential for addressing complex challenges in the tech industry.
Pursuing his Doctor of Philosophy at Rice University was a significant milestone in Sitaram's academic career. His research during this time focused on the intersection of software engineering and artificial intelligence, a field that has immense implications for the future of technology. Sitaram's contributions to this research area are not only noteworthy but also pave the way for innovative developments and applications.
Notable Contributions
Throughout his career, Sitaram has made substantial contributions to the tech community. His work at Google is characterized by a deep understanding of software architecture and development processes, which he utilizes to create systems that facilitate better communication, enhance data processing, and improve overall system performance. Sitaram's engineering cultivates a culture of innovation and collaboration, embracing the dynamic nature of the tech industry.
Additionally, Sitaram is passionate about mentoring the next generation of software engineers. He frequently shares insights from his own experience, helping students and young professionals navigate their career paths. His commitment to education reflects his belief in the importance of giving back to the community and fostering talent within the tech sector.
Sitaram's journey in the ever-evolving landscape of technology showcases his dedication to professional growth and his exceptional capability as a software engineer. His proficiency in both foundational and contemporary technological paradigms makes him a valuable asset to any team and underscores his influence in the field.
Highlights
Wanted to share that I've joined Glean—and jumped straight into the warmest of onboardings at the company’s offsite in Tahoe. Over a few days and countless conversations, I felt waves of nostalgia from Google circa 2003. There is similar positive energy and fearless optimism, and top-notch talent that is both exciting and humbling. The company size is about the same (around 500) as Google was back then, and I used my same poor dancing skills with another great band reminiscent of Tainted Love. Full circle, new beginnings.
Glean's mission around "Put AI to work. At work" resonates deeply with me—much like Google's mission for the public web. The idea of making all workplace knowledge accessible and actionable is both broad and deep, with limitless possibilities for product and engineering. This time, we have AI as a powerful ally, backed by Glean's five years of groundwork and traction. I'm delighted to see many of my initial ideas already taking shape, and eager to work with an exceptional team and technology.
I'm grateful to hundreds of people I've spoken with in the last 18 months who have shaped my journey to this point and led to immense personal growth—thank you all, hope to catch up again soon. And in these first few days at Glean, I've had remarkable conversations with Arvind Jain, Tamar Yehoshua, T.R. Vishwanath, and many others, connecting the dots across sales, product, design, and engineering. I'm excited—much more than I expected for round two of my career—by what’s ahead.
𝐇𝐞𝐥𝐩𝐢𝐧𝐠 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬𝐞𝐬 𝐀𝐜𝐡𝐢𝐞𝐯𝐞 𝐈𝐦𝐦𝐞𝐝𝐢𝐚𝐭𝐞 𝐖𝐢𝐧𝐬 𝐔𝐬𝐢𝐧𝐠 𝐀𝐈 (𝐭𝐡𝐫𝐨𝐮𝐠𝐡 samvid.ai)
Businesses are built upon contracts, agreements, and reports, rich with facts and figures. Every business owner knows that critical knowledge obtained by summarizing and querying these documents can significantly boost productivity and mitigate risks. OpenAI’s file_search API and Snowflake’s document AI offer vector and text search with RAG and client-specific models. These technologies improve the status quo but may not fully meet your day-to-day business needs.
Your business’ pain points might include: ➢ analyzing and forecasting data such as agricultural yield or financial projections. ➢ computing complex formulas such as shipping cost or carbon footprint from data in reports plus “world knowledge.” ➢ evaluating and negotiating contracts by assessing key levers and tradeoffs and leveraging historical data.
Serving these deeper needs requires high-precision data extraction and schema inference to enable computation and reasoning. Azure AI, DocSumo, and Eigen AI offer extraction, however, they are general extractors by document type. Whereas, hyper-customized extraction and reasoning won’t scale, and generic extraction of numerical and factual data, even with RAG, isn’t good enough to reason with or compute from.
I’m helping samvid.ai push this envelope, with a product that scalably meets clients’ business needs, initially targeting SMBs. This tool extracts data from uploaded documents, infers schema with domain knowledge, and generalizes the ensuing high-level abstractions across domains. As table-stakes, we offer a 2-level nested summary and a chatbot over uploaded documents, with text and semantic search and RAG for baseline accuracy. We significantly improve precision over RAG by extracting the key facts (“essence”) of the document into a knowledge graph and using OpenAI’s or Anthropic’s “function calling” API to feed it back into the chatbot and reasoning engines. This ensures the accuracy required for customers in AgriTech, Logistics, Sustainability, and other domains. E.g., in Logistics, our system identifies discount tiers and surcharges for a parcel shipment based on dimensional weight (calculated weight based on package volume) and the dim factor specified in the contract, something RAG may miss.
With LLMs, we can reduce customer onboarding cost by distilling customer-agnostic “templates” from various types of extractors and extending these templates to other clients. This approach ensures that no client’s sensitive data is exposed to another, thus preserving privacy and isolation. This third-level application of AI gives us safe scalability while staying closely aligned to client needs. With this approach, we can create tailored extractors and implement diverse vertical use cases with low manual effort.
Please check out the demo video, or contact Ram Bala or Arun Rao for a full demo of these use cases.

