Who Is Uday Ruddarraju? Hyderabad-Educated Tech Executive Takes Charge of OpenAI Computing Business

A Hyderabad-educated engineer is taking charge of the infrastructure behind ChatGPT. On 14 October 2026, OpenAI named Uday Ruddarraju to lead its compute and data-centre operations. Sachin Katti, whose responsibilities he will take over, plans to leave his leadership role on 15 November but stay on as an adviser.

He already became CTO of Compute in July. October’s decision expands his authority to commercial strategy and infrastructure delivery, as confirmed in Bloomberg’s October 14 report.

Key Highlights

  • OpenAI expanded Ruddarraju’s leadership role on 14 October 2026.
  • He studied at CBIT in Hyderabad and later at the University of Minnesota.
  • His career includes eBay, Robinhood, and Elon Musk’s xAI.
  • Sachin Katti’s final day is scheduled for 15 November 2026; he will remain an adviser.

Who Is Uday Ruddarraju? From Hyderabad to OpenAI

Ruddarraju studied computer science at Chaitanya Bharathi Institute of Technology (CBIT), Hyderabad, before completing a master’s degree in computer science at the University of Minnesota. His professional record is centred on infrastructure engineering rather than building consumer-facing apps.

After an Amazon internship, he built infrastructure systems across four companies:

  • 2013–2018, eBay: Worked on cloud platforms and infrastructure engineering.
  • 2018–2024, Robinhood: Rose through engineering roles to lead infrastructure teams at the trading platform.
  • 2024–2025, xAI: Headed infrastructure engineering, working on large-scale computing for AI models, including the Colossus project.
  • July 2025, OpenAI: Joined to lead compute and infrastructure; became CTO of Compute in July 2026.

His education and career progression were detailed in India Today’s 15 October profile, while Data Center Dynamics reported his earlier promotion in July.

At xAI, he helped support GPU clusters for training AI models. OpenAI now needs that engineering experience on projects that also depend on construction schedules and power supplies.

What Changed in OpenAI’s Leadership on 14 October?

Katti, who spent roughly a year at OpenAI, oversaw infrastructure strategy and commercial deals. Ruddarraju will report to OpenAI president Greg Brockman, The Information reported.

The new structure brings several responsibilities closer together:

  • Engineering: Making computing, networking and storage systems work reliably.
  • Infrastructure delivery: Coordinating data-centre projects as sites are built and brought online.
  • Commercial strategy: Working with suppliers and partners to secure the capacity OpenAI needs.

Katti will remain an adviser. Ruddarraju already worked with engineering and construction teams, making this an expanded assignment rather than a completely unfamiliar role.

Why Does OpenAI Need So Much Computing Power?

A new AI model needs thousands of specialised chips working together during training. Once released, it also needs computing capacity to answer requests. Buying hardware is only part of that job. The machines need electricity, cooling, network connections, and systems that prevent failures from disrupting work.

The scale is visible in OpenAI’s own numbers. In April 2026, the company said it had already secured more than 10 gigawatts of US AI infrastructure under its Stargate programme, ahead of a goal originally set for 2029. That figure refers to secured infrastructure capacity; it does not mean all 10 gigawatts were operating in April.

A separate January 2026 agreement with SB Energy included a 1.2-gigawatt data-centre lease. OpenAI also said its GPT-5.5 model was trained at the Stargate campus in Abilene, Texas, illustrating how large physical facilities feed directly into model development.

Both figures appear in OpenAI’s April infrastructure update and its January SB Energy announcement.

A signed capacity deal cannot run a model. Sites still need construction, electricity, equipment, and reliability testing. A facility might have promised chip deliveries but lack the power connection required to start them. That is why an infrastructure leader needs construction and supplier coordination as well as software expertise.

What Will Ruddarraju Need to Deliver Next?

The immediate test is the gap between announced capacity and working machines. OpenAI’s June 2026 groundbreaking of a 1-gigawatt Michigan campus is one example: breaking ground shows a project has started, not that its full output is available.

Ruddarraju’s teams will have to coordinate facilities, hardware deliveries, and computing software while the company continues training models and handling demand for ChatGPT. Commercial negotiations now fall more directly within that work. Bloomberg reported that he expected to spend more time on that side of the business.

The useful numbers to watch are operational capacity and confirmed site openings, not just announced gigawatts. His Hyderabad background is notable; delivering the infrastructure is the tougher test.

For further developments, follow OpenAI’s official infrastructure announcements and independently reported updates on the leadership transition.

Who Is Uday Ruddarraju?

Uday Ruddarraju is an Indian-origin infrastructure engineer and OpenAI’s CTO of Compute. In October 2026, his responsibilities expanded to cover the company’s wider computing and infrastructure operations.

Where Did Uday Ruddarraju Study?

He studied computer science at Chaitanya Bharathi Institute of Technology in Hyderabad and later completed a master’s degree in computer science at the University of Minnesota.

When Did Uday Ruddarraju Join OpenAI?

He joined OpenAI in July 2025 after working at xAI. He became CTO of Compute in July 2026, before receiving wider infrastructure responsibilities that October.

Who Is Uday Ruddarraju Replacing at OpenAI?

He is taking over responsibilities previously managed by Sachin Katti. Katti plans to leave his leadership role on 15 November 2026 and remain an adviser.

What Is OpenAI’s Stargate Project?

Stargate is OpenAI’s programme to secure large-scale AI computing infrastructure. It involves data centres, electricity, chips, and partners needed to train and serve increasingly demanding models.

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