Your Native Language Might Just Be Your Next Tech Skill

When global tech giants began competing to develop large language models, the narrative often focused on English and other widely spoken languages. In India though, a country with 22 official languages and hundreds of regional dialects, AI systems face various challenges in understanding linguistic nuance, regional idioms, and terminology.

These challenges have led to an emerging role — that of an Indic language expert. Part linguist, part cultural curator, part AI trainer, these professionals are increasingly vital for startups and enterprises building multilingual, domain-specific AI systems. They will have to work far beyond translation, ensuring that AI understands context, tone and cultural subtlety.

Why Indic Language Experts Matter?

AI models thrive on large datasets. For English or Mandarin, vast corpora are available online. But for Bhojpuri business terms, Tamil healthcare phrases, or Assamese legal jargon, digital resources are fragmented at best. Indic language experts help fill this void.

“At HCL GUVI, an EdTech platform offering technology upskilling courses in regional languages, the primary focus is on creating a democratised technological ecosystem,” said Arun Prakash M, founder & CEO of HCL GUVI.

Prakash said they are building multilingual LLMs like Code Catalyst using small, hyperlocal models guided by language and subject matter experts. “The learners trained on these systems will be the first line of innovators who spearhead AI training and localisation in the coming years.”

These experts not only ensure linguistic accuracy but also bring cultural fluency. “Students from non-English backgrounds bring unique perspectives,” said Prakash, adding that studies have shown comprehension improves significantly in one’s mother tongue. 

“Learners operating in their native language raise challenges and solutions that drive deeper personalisation and localisation in AI systems,” he said.

The role differs substantially from traditional translation. Indic language experts often annotate datasets, validate outputs, and fine-tune domain-specific models. They might review raw voice data to tag regional intonation, or verify that a chatbot’s responses sound natural rather than stilted.

Nakul Kundra, co-founder of Devnagri AI, said they have developed a dataset of over 500 million sentences across Indian languages, capturing dialects, idioms, and domain-specific terminology. “That combination of large-scale data and sophisticated training methods allows our models to generate content that is contextually accurate and culturally relevant for real-world use.”

Forthcoming Roles and Responsibilities

While Devnagri’s AI systems are designed to handle most linguistic variation autonomously today, Kundra sees the potential for future formalised roles. “As AI adoption deepens across governance, healthcare, and education, positions like ‘AI language specialist’ or ‘dialect and domain curator’ may emerge, with structured paths, certifications, and responsibilities,” he explained.

The emergence of these roles has been highlighted by recent job postings from Indian startups and edtech firms. Recently, Micro1 announced openings for Indic Language Experts in Marathi, Telugu, Tamil, Bengali, and Hindi.

Creating accurate, culturally relevant responses in various regional languages, they review AI outputs, annotate data, and provide local context. Roles are often remote with flexible time commitments, and prefer candidates with language, teaching, or translation experience.

Vernacular education platforms like HCL GUVI are directly linking technical upskilling with multilingual AI expertise. Prakash said that for learners in remote regions of India or sub-Saharan Africa, technical training alone isn’t enough. “They need domain-specific, multilingual AI expertise to stand shoulder to shoulder with global peers.”

The demand is driven by a rapidly expanding AI ecosystem, spanning sectors such as healthcare, banking, insurance, and government. Multilingual AI solutions can increase reach, enhance trust, and avoid errors that arise when systems fail to account for regional or cultural context.

The road ahead is not devoid of hurdles; the talent pool is limited, and salaries often lag behind core engineering roles. Ethical considerations also arise, including the proper recognition of contributors behind AI outputs. 

Linguistic Diversity an Asset

For a country like India, with over 450 languages and more than 1,600 documented dialects, the government has launched initiatives to advance AI in Indian languages. These include AI4Bharat, an open-source project at IIT Madras supporting speech recognition, translation, and language understanding, and BharatGPT by CoRover.ai, a sovereign generative AI platform with multilingual capabilities.

Suraj Amonkar, chief AI research & platforms officer at Fractal, told Economic Times that linguistic diversity is one of India’s greatest assets in AI and Indian linguists are uniquely positioned to shape how AI serves more than a billion people, making it more inclusive across sectors. 

Kundra, however, highlighted a practical challenge: “High-quality datasets are scarce, especially for technical sectors like banking or law. AI often struggles with context and cultural nuance.” 

Language experts help by annotating datasets, guiding vocabulary, and ensuring compliance, helping AI generate precise, reliable, and consistent outputs across languages and domains, he said.

Looking forward, vernacular tech education and Indic language expertise may become India’s strategic advantage. Prakash observed localised AI models are already present in India. 

“In the long term, culturally aware, multilingual AI may well become India’s ace up the sleeve, tapping into talent from every corner of the country,” he concluded.

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