Experience: 5-8 Years
Location: Noida
Role Overview
As the ML Engineer, you will adapt and optimise foundation models for personalised learning in Indian languages and educational contexts. You own the complete fine-tuning lifecycle: data curation, training runs, evaluation, and deployment of customised LLMs that power the learning experience.
Key Responsibilities
- Fine-tune foundation models for education-specific tasks: question answering, content generation, adaptive feedback, and curriculum alignment.
- Own end-to-end fine-tuning workflows: dataset curation, training runs, hyperparameter tuning, evaluation, and model versioning.
- Implement efficient fine-tuning methods (LoRA, QLoRA, DoRA, adapters) appropriate to available compute budgets.
- Build RLHF and preference optimisation pipelines: DPO, PPO, reward modelling for aligning models to learning outcomes.
- Optimise GPU training efficiency: DeepSpeed, FSDP, gradient checkpointing, mixed precision, multi-GPU setups.
- Evaluate fine-tuned models rigorously: perplexity, task-specific benchmarks, human eval, and regression testing.
- Build data pipelines for instruction tuning datasets including multilingual and Indic language data.
- Assess new open-source model releases for domain applicability and adoption readiness.
- Define and track model performance metrics, evaluation benchmarks, and optimisation targets across all fine-tuned model versions.
- Work with open-source and sovereign LLMs, owning the full model adaptation lifecycle using proven, industry-standard frameworks.
Must-Have Skills
- Strong experience in fine-tuning and optimizing Large Language Models (LLMs)
- Hands-on experience with LoRA, QLoRA, SFT, DPO, RLHF, or similar fine-tuning techniques
- Proficiency in PyTorch and Hugging Face ecosystem (Transformers, PEFT, TRL)
- Experience with distributed and multi-GPU model training
- Strong understanding of model performance, evaluation, and optimization
- Knowledge of DeepSpeed, Megatron-LM, and large-scale training frameworks
- Understanding of AI/ML pipelines, data preparation, and model deployment
- Experience working with open-source and sovereign LLMs; focus on standard, production-proven frameworks
- This role is part of a 0-to-1 vertical build. The person joining should be comfortable with ambiguity, high ownership, rapid iteration, hands-on execution, team building, and periods of high-intensity work during the early setup phase