Sarvam AI is India's leading indigenous AI lab, building large language models (Sarvam-1, Sarvam-2B) and voice AI systems in Indian languages including Hindi, Tamil, Telugu, Kannada, and Bangla.
ML engineer interviews at Sarvam AI combine strong NLP fundamentals with production LLM deployment challenges unique to Indian language contexts: character tokenisation for Devanagari, ASR for code-switched speech, and low-latency TTS for mobile-first users. In 2026 expect coding in Python, deep NLP/ML theory, and applied design questions about training and serving multilingual models.
About Sarvam AI
Sarvam AI (formerly Sarvam) is India's leading vernacular AI company, building Indic language models (BharatGPT), voice AI, and the Sarvam-2B open-source model. Powers government-scale AI deployments.
Apply via Sarvam AI careers page or get sourced; founder or research lead screen
Technical coding and NLP theory assessment
Deep technical interviews on LLM training, inference, and Indic language challenges
Research presentation or take-home followed by culture and mission fit discussion
Round 1 (45-60 min)
technical screen on ML fundamentals, Python coding, and a discussion of your most relevant NLP or speech project.
Round 2 (60-75 min)
deep NLP interview covering transformer internals, fine-tuning methodologies, and Indic language processing challenges.
Round 3 (60 min)
applied design round on training or serving a multilingual LLM or ASR system for Indian language users at mobile scale.
Round 4 (30-45 min)
culture and mission round on why indigenous Indian AI matters, your views on AI safety for low-resource languages, and long-term research interests.
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Sign up free: unlock all questionsThe typical Sarvam AI recruitment process has 4 stages: Apply via Sarvam AI careers page or get sourced; founder or research lead screen → Technical coding and NLP theory assessment → Deep technical interviews on LLM training, inference, and Indic language challenges → Research presentation or take-home followed by culture and mission fit discussion.
Sarvam AI typically conducts 4 interview rounds: Round 1 (45-60 min): technical screen on ML fundamentals, Python coding, and a discussion of your most relevant NLP or speech project.; Round 2 (60-75 min): deep NLP interview covering transformer internals, fine-tuning methodologies, and Indic language processing challenges.; Round 3 (60 min): applied design round on training or serving a multilingual LLM or ASR system for Indian language users at mobile scale.; Round 4 (30-45 min): culture and mission round on why indigenous Indian AI matters, your views on AI safety for low-resource languages, and long-term research interests..
HireStepX recommends the Indic AI Depth framework for this type of interview: Pair strong NLP and deep learning fundamentals with practical knowledge of Indic language challenges: tokenisation, transliteration, ASR for code-switching, and efficient LLM inference for mobile
To answer this question well, HireStepX recommends the Indic AI Depth approach: Pair strong NLP and deep learning fundamentals with practical knowledge of Indic language challenges: tokenisation, transliteration, ASR for code-switching, and efficient LLM inference for mobile Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Indic AI Depth approach: Pair strong NLP and deep learning fundamentals with practical knowledge of Indic language challenges: tokenisation, transliteration, ASR for code-switching, and efficient LLM inference for mobile Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Indic AI Depth approach: Pair strong NLP and deep learning fundamentals with practical knowledge of Indic language challenges: tokenisation, transliteration, ASR for code-switching, and efficient LLM inference for mobile Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Indic AI Depth approach: Pair strong NLP and deep learning fundamentals with practical knowledge of Indic language challenges: tokenisation, transliteration, ASR for code-switching, and efficient LLM inference for mobile Ground your answer in a specific real example from your own experience.
To answer this question well, HireStepX recommends the Indic AI Depth approach: Pair strong NLP and deep learning fundamentals with practical knowledge of Indic language challenges: tokenisation, transliteration, ASR for code-switching, and efficient LLM inference for mobile Ground your answer in a specific real example from your own experience.