Skill specialists
Hire Vetted Hugging Face Engineers
Find NLP and LLM engineers with deep Hugging Face Transformers experience for open-source fine-tuning, model deployment, and inference optimisation without proprietary API costs.
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Uriel
Khandu
Viren
Khaleelur
Jahir
Gulshat
Dzunisani
Alex
Daniel
Aditya
Hiring guide
Why companies hire Hugging Face experts
Hugging Face has become the central hub for open-source AI — housing thousands of models, datasets, and tools. Engineers with Hugging Face expertise can integrate state-of-the-art models into your product without the cost of proprietary APIs.
Popular projects
- Fine-tune an open-source LLM to match your domain or brand tone
- Replace costly OpenAI API calls with a self-hosted open model
- Build NLP pipelines for classification, NER, or summarisation
- Quantise and optimise a model for faster, cheaper inference
What to look for
- Hands-on experience with Transformers, PEFT, and TRL libraries
- Knowledge of efficient fine-tuning methods (LoRA, QLoRA)
- Experience deploying models via Inference Endpoints or self-hosted
Frequently asked questions
What can a Hugging Face engineer do that a general ML engineer cannot?
A Hugging Face specialist knows the ecosystem deeply — model cards, tokenisers, training arguments, PEFT methods, and deployment tooling. This depth speeds up fine-tuning and deployment significantly compared to building from scratch.
Is it cheaper to use open-source models via Hugging Face than the OpenAI API?
For high-volume inference, yes. Engineers who know Hugging Face can help you evaluate the cost-quality trade-off and deploy the right open-source model for your use case.
Which open-source models do Hugging Face engineers typically work with?
Most engineers on RemoteAI have experience with LLaMA, Mistral, Phi, Gemma, and Qwen families, plus domain-specific models for NLP tasks like classification and NER. Check individual profiles for specific model experience.
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