Role Overview :
You will lead the fine-tuning and evaluation of open-source LLMs (e.g., Llama, Mistral, Qwen). This role requires hands-on skills in Python, PyTorch, Hugging Face, and distributed training :
- Fine-tune and adapt open-source LLMs using LoRA / QLoRA.
- Build pipelines for supervised fine-tuning and preference optimization (DPO / RLHF).
- Evaluate model performance using standard and domain-specific benchmarks.
- Optimize inference with quantization and serving frameworks (vLLM, TGI).
- Collaborate with data engineers on preprocessing and :
- Experience : 3-6 years in ML / NLP
- Strong coding in Python and PyTorch.
- Experience with Hugging Face, DeepSpeed, or similar frameworks.
- Familiarity with distributed training and GPU optimization.
- Knowledge of transformers, attention mechanisms, and NLP workflows.
- Bonus : Experience in domain-specific fine-tuning (finance, legal, etc.).
(ref : hirist.tech)