Sahaj Putcha

Most engineers pick a lane. Sahaj Putcha picked two at once. At the University of Illinois Urbana-Champaign, he double-majored in Computer Science and Astronomy - not as a philosophical statement, but as a practical bet that understanding data at galactic scale would make him better at understanding data at enterprise scale. By the time he graduated in 2021, he had already worked at NASA.

That NASA stint happened in the summer of 2017, during his undergraduate years at UIUC. He landed at NASA Ames Research Center in Silicon Valley - the same campus where researchers work on planetary entry systems and autonomous vehicle navigation. His role: software engineering intern. Ames runs on the assumption that the data you miss is the data that matters most. It's a philosophy that travels well.

Around the same time, Sahaj was also doing hardware engineering at SMART Modular Technologies, a memory and storage manufacturer with roots in Silicon Valley. The combination - software at NASA, hardware at SMART - is unusual for an undergrad. Most students pick one. He apparently had other plans.

Back at Illinois, he joined the Fighting Illini Data Team, where sports analytics met real-world data modeling. Then came Accenture, where he did data analysis and dashboard engineering - the unglamorous, essential work of turning messy numbers into decisions. Then Amazon Lab126, the secretive R&D group behind Kindle, Echo, and the company's hardware skunkworks. He was designing and integrating algorithms for emerging technologies at the lab that invented the Alexa hardware stack.

By June 2021, degree in hand, he joined Amazon proper as a Software Development Engineer. Not in cloud infrastructure. Not in e-commerce. In consumer robotics - specifically, building full-stack customer experiences for the kind of physical devices that Amazon was quietly developing alongside Astro, its household robot. For two-plus years, he wrote Android and Java code for systems that operate in the physical world, where a software bug can mean something actually breaks.

In September 2023, he stepped away from Amazon. Four months later, he joined Productiv.

Productiv is a SaaS management platform that has raised $73 million, including a $45 million Series C in early 2021. The company's core product answers a question that sounds obvious until you try to answer it: what software is your company actually using, who's using it, what is it costing, and is any of it a risk? For modern enterprises with hundreds of SaaS subscriptions, shadow AI tools, and overlapping licenses, that question is harder than it sounds.

Sahaj joined the eight-person software engineering team at Productiv in January 2024, arriving as the company was sharpening its focus on AI visibility and shadow AI detection. The platform that was once primarily about SaaS spend management was expanding to track AI tools - because enterprises were adopting ChatGPT, Claude, Gemini, and dozens of specialized AI applications without their IT teams knowing. The risk wasn't hypothetical. Data was flowing to external AI systems without contracts, governance, or even basic logging.

The work at Productiv suits someone who has operated at the intersection of data, systems, and human behavior. NASA taught him to care about what goes undetected. Amazon taught him to build for scale and for real users. UIUC's Astronomy program taught him that the most interesting signals are often hidden in noise.

At a company like Productiv - 160 employees, a clear product mandate, serious funding, and a market moment in AI governance - a software engineer carries real weight. The platform that surfaces shadow AI and rationalizes SaaS portfolios for enterprise customers is only as good as the code that makes it work. Sahaj is part of the team making that case in production, daily.

He is based in San Francisco, the city that writes the software the rest of the enterprise world then has to manage. There is a certain symmetry in that.

Productiv was founded by Jody Shapiro (CEO), Munish Gandhi (COO), and Ashish Aggarwal (CTO) - a trio with roots at Google Analytics, eBay, Amazon, LinkedIn, and Microsoft. The company operates in a space that emerged from a specific enterprise pain: most organizations pay for software nobody uses, and nobody knows what AI tools their employees have quietly adopted.

The platform integrates with SSO systems to provide real-time visibility across an organization's entire application portfolio. It surfaces usage data, flags shadow IT and shadow AI, manages contracts and renewals, and provides benchmarking data so procurement teams know if they're paying too much. For enterprises managing 200+ SaaS subscriptions, it replaces the spreadsheet-and-prayer approach with something that actually works.

Productiv's customers include enterprise organizations that need to answer questions like: Which AI tools are employees using that we haven't approved? Which of our 250 SaaS subscriptions have fewer than 10 active users? Are any of our applications sending sensitive data to external AI systems without a contract in place?

The company's pivot toward AI governance - tracking not just SaaS but the expanding galaxy of AI tools entering enterprise environments - positions it directly in the path of one of the defining IT challenges of the next decade. Sahaj joined right as that pivot was accelerating.