📋 Description

• Fine-tune and train Small Language Models (SLMs) using Hugging Face, TRL, PEFT
• Experiment with architectures, training strategies, and datasets
• Optimize for efficient inference via quantization, pruning, distillation
• Prepare and deploy lightweight AI models to edge/mobile/local servers
• Design end-to-end MLOps pipelines for data, training, deployment, monitoring
• Build reliable, repeatable ML workflows for production use

🎯 Requirements

• Hands-on experience training/fine-tuning Small Language Models or transformers
• Strong knowledge of Hugging Face, LoRA, QLoRA, PEFT
• Experience optimizing models for inference (quantization/pruning/distillation)
• Experience deploying ML models to edge/mobile/local environments
• Understanding of end-to-end MLOps from ingestion to monitoring
• Experience monitoring accuracy, latency, and hardware utilization

🎁 Benefits

• Work with modern AI tech focused on Small Language Models
• Hands-on exposure to fine-tuning, optimization, compression, efficient inference
• Build production-grade MLOps pipelines across the ML lifecycle
• Experience edge, mobile, and local AI deployment environments
• Exposure to Hugging Face, TRL, LoRA, QLoRA, PEFT, and MLOps tooling
• Contribute to scalable AI solutions for real-world production