AI/LLM Fine-Tuning Engineer – Arabic, Egyptian Arabic, Gulf Arabic & English Smart Home Model
Budget / Salary$30–250
TypeFreelance project
LocationRemote
Posted1 hour ago
Freelancer Project Message — ~1000 characters
Hello, I’m looking for an experienced LLM/NLP Fine-Tuning Engineer to develop a specialized language model for MEGA Smart Home AI.
The model should understand natural smart-home commands in:
Egyptian Arabic
Gulf Arabic
English
User sentences will be Arabic-only or English-only; Arabic-English code-switching is not required.
The main goal is not general conversation, but Natural Language Understanding for smart-home control. The model must identify intent, device, location, action, and parameters, then generate a strict structured JSON output compatible with our MEGA OS.
It must also handle ambiguous requests by asking for clarification instead of guessing, and recognize unknown/unsupported devices or commands.
Required experience: SFT, LoRA/QLoRA, LLM fine-tuning, Arabic NLP/NLU, Egyptian/Gulf Arabic, dataset creation/annotation, Python, evaluation and benchmarking.
Please provide:
1. Previous Arabic/Egyptian/Gulf LLM projects.
2. Recommended base model and why.
3. Fine-tuning methodology.
4. Dataset strategy and expected size.
5. Evaluation methodology and metrics.
6. Final trained model and inference/deployment files.
This is intended for local/offline deployment on MEGA hardware, so model size and quantization efficiency are important.
Hello, I’m looking for an experienced LLM/NLP Fine-Tuning Engineer to develop a specialized language model for MEGA Smart Home AI.
The model should understand natural smart-home commands in:
Egyptian Arabic
Gulf Arabic
English
User sentences will be Arabic-only or English-only; Arabic-English code-switching is not required.
The main goal is not general conversation, but Natural Language Understanding for smart-home control. The model must identify intent, device, location, action, and parameters, then generate a strict structured JSON output compatible with our MEGA OS.
It must also handle ambiguous requests by asking for clarification instead of guessing, and recognize unknown/unsupported devices or commands.
Required experience: SFT, LoRA/QLoRA, LLM fine-tuning, Arabic NLP/NLU, Egyptian/Gulf Arabic, dataset creation/annotation, Python, evaluation and benchmarking.
Please provide:
1. Previous Arabic/Egyptian/Gulf LLM projects.
2. Recommended base model and why.
3. Fine-tuning methodology.
4. Dataset strategy and expected size.
5. Evaluation methodology and metrics.
6. Final trained model and inference/deployment files.
This is intended for local/offline deployment on MEGA hardware, so model size and quantization efficiency are important.
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