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Remote, popular ML title increases applicants, but niche SLM and edge specialization reduces overall density.
Specialized LLM fine-tuning, quantization, and edge deployment needs limit cross-industry transferability.
Requires specialized SLM fine-tuning, optimization, edge deployment, and MLOps expertise, enforcing strict technical filters.
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Fine-tune and train small language models (SLMs) using Hugging Face and adapter methods like LoRA, QLoRA, PEFT.
Optimize SLMs for inference through quantization, pruning, and knowledge distillation to meet strict latency targets for deployment on edge devices, mobile, and local servers.
Build and manage end-to-end MLOps pipelines from data ingestion through deployment and monitor production metrics including accuracy, latency, and hardware utilization.
Experience with training and fine-tuning SLMs using Hugging Face and adaptor technologies.
Skills in model optimization techniques: quantization, pruning, knowledge distillation.
Ability to deploy machine learning models on edge devices, mobile platforms, or local servers.
Work Experience Required: Not explicitly mentioned in the JD.
Hands-on expertise in deploying and optimizing lightweight language models for performance-constrained environments (edge/mobile).
Experience in building comprehensive MLOps pipelines and monitoring ML model health in production at scale.
Technical proficiency with model performance evaluation frameworks and production-grade latency and hardware usage monitoring.