Uber to Build Two Engineering Campuses in India by 2027

Uber announced on May 14 that it will expand its engineering presence in India with two new campuses in Bengaluru and Hyderabad, expected to accommodate approximately 9,600 employees by the end of 2027. The company also plans to establish its first local data center through a partnership with Indian conglomerate Adani Group, with the facility scheduled to go online in Q4 2026. The expansion reflects Uber’s commitment to India as a major engineering hub as it competes with local ride-hailing player Rapido and diversifies beyond ride-hailing services.

Expansion and Hiring Focus

Uber currently employs approximately 3,500 people in India. The company is actively hiring for roles in artificial intelligence, machine learning, autonomous vehicle operations, and back-end infrastructure. These hiring efforts align with Uber’s broader strategy to build AI and machine learning capabilities within the Indian market.

Data Center Partnership with Adani Group

The partnership with Adani Group represents a strategic approach to establishing computing infrastructure in India. Rather than building independently, Uber chose to collaborate with the Indian conglomerate, which operates across infrastructure, energy, and logistics sectors. This partnership provides Uber access to land and power resources, both critical requirements for data center operations.

The Adani data center project is part of a broader infrastructure initiative in India. Adani Enterprises is simultaneously developing what has been described as India’s largest AI data center campus in Visakhapatnam through AdaniConneX, involving Google, Bharti Airtel (an Indian telecom operator), and other partners, with an estimated investment of US$15 billion spanning 2026 to 2030.

Strategic Context

Uber’s India expansion follows a US$330 million investment in its India unit earlier in 2026. India has become a major engineering base for global technology firms, and this expansion underscores the growing importance of local computing capacity driven by data localization requirements and increasing demand for AI infrastructure.