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Popular ML title with mid-level hiring bracket but niche LLM specialization yields moderate competition.
Core LLM engineering skills transfer across industries, though education-domain familiarity moderately increases fit.
Multiple mandatory LLM, RLHF, and distributed-training technical requirements make screening highly selective.
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Own end-to-end fine-tuning lifecycle of Large Language Models (LLMs) for education-specific tasks including dataset curation, training, evaluation, and deployment.
Develop and optimize efficient fine-tuning methods (e.g., LoRA, QLoRA) and reinforcement learning pipelines (DPO, PPO, reward modeling) tailored to Indian languages and education contexts.
Define and track model performance metrics and deploy customized models to power personalised learning experiences at scale.
Strong hands-on experience with fine-tuning and optimizing Large Language Models using methods like LoRA, QLoRA, SFT, DPO, RLHF.
Proficiency with PyTorch and Hugging Face ecosystem (Transformers, PEFT, TRL).
Experience in distributed and multi-GPU training and familiarity with frameworks like DeepSpeed and Megatron-LM.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in managing complete ML model adaptation lifecycle including data preparation, training, evaluation, and deployment in production.
Comfortable operating in ambiguous, early-stage project environments with high ownership and rapid iteration.
Domain expertise or strong interest in AI applications in education, especially for Indian languages and public education infrastructure.