India has 22 official languages and hundreds of dialects.

Building AI that works across all of them is one of the most fascinating and important challenges in NLP.

Most AI models are English-first. Performance degrades significantly for Hindi, Tamil, Bengali, and other Indian languages. Not because the models can't learn these languages — because they're not trained on enough data in them.

What's changing in 2026: multilingual models are getting better fast. IndicBERT and similar models trained specifically on Indian languages show dramatic improvements. Translation quality has reached the point where translate-then-process pipelines work surprisingly well.

But there are unique challenges: code-switching (mixing Hindi and English in the same sentence is natural for millions of Indians), script diversity (Devanagari, Tamil, Bengali, and more), dialectal variation within languages, and significantly less labeled training data.

The engineers working on Indic NLP are solving problems that have global implications. The techniques for handling low-resource languages apply to hundreds of languages worldwide.

If you speak an Indian language and have ML skills, you're positioned to work on one of the most impactful problems in AI. The models need people who understand the linguistic nuances that English-speaking researchers miss.

Multilingual AI isn't a niche. In a country of 1.4 billion people, it's the mainstream.