Newsletter

India’s AI/ML talent landscape is expanding rapidly, with the AI workforce growing across major technology hubs

India’s festive season brings a sharp rise in shopping activity, creating a demanding period for retail, FMCG, e-commerce and quick-commerce businesses.

India engineering talent represents a 2.1M+ professional pool across engineering and automotive-related roles, with moderate talent availability and meaningful professional mobility.

Big Data has evolved from a technology challenge into a business and talent challenge. As organisations generate massive volumes of data from customers, applications, devices, and digital platforms, the ability to collect, process, and turn that data into insights has become a competitive advantage. But technology alone cannot deliver this value.

India’s EV boom is creating a talent squeeze. Explore the 5 engineering skills automakers need and how hiring and reskilling can close the EV talent gap.

India’s insurance sector is hiring — but not uniformly. Insurance hiring in India is becoming increasingly segmented, with technology and data talent seeing stronger demand while traditional insurance and operational hiring continues to expand at a steadier pace.

A leading bank required a technology leader who understood AI and digital products, but who could also navigate the complexities of risk and regulation. The search revealed a bigger truth

A customer approved what sounded like an urgent call from their bank manager. The voice was identical, the tone familiar, the request completely genuine-sounding. It wasn’t human – it was AI-generated.

A bank had invested millions in AI-powered fraud detection. Weeks before launch, the project stalled – not because the technology failed, but because two critical cybersecurity roles had sat vacant for over four months unfilled. The biggest risk wasn’t the software. It was the missing talent behind it.

Picture a Monday morning at a mid-sized private bank. A loan officer logs in and reviews five credit applications — but before she reads a single line, an AI model has already scored each borrower using 400+ data signals, flagged two anomalies in transaction history, and cross-referenced all five against AML watchlists.