What you'll do

- Fine-tune and train SLMs using Hugging Face, TRL, and adapter methods (LoRA, QLoRA, PEFT)
- Optimize models for inference via quantization, pruning, and knowledge distillation
- Deploy models to edge devices, mobile, and local servers with strict latency targets
- Build end-to-end MLOps pipelines from data ingestion to deployment
- Monitor model accuracy, latency, and hardware utilization in production
- Evaluate model quality using benchmarking frameworks and custom evaluation suites

Your Qualifications

- SLM Development Fine-tuning: Train and fine-tune SLMs using Hugging Face and Knowledge on Adaptors.
- Model Optimization: Apply quantization, pruning, knowledge distillation, and optimization for lightweight, productive models.
- Edge Deployment: Deploy models to edge devices, mobile, and local servers, etc.




- Pipeline Engineering: Build end-to-end MLOps pipelines - from data ingestion to deployment.
- Performance Monitoring: Track model accuracy, latency, and CPU/GPU usage in production.

Good to have

- Deployment experience on edge or mobile environments
- Knowledge of ONNX export and cross-platform inference
- MLOps tooling - experiment tracking, model registries, CI/CD for ML

Disclaimer: This job posting & Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Machine Learning Engineer - AI (India)
🏢 Egnyte
📍 India