LLM Engineer
Company shown to members 🔒 · Remote · Philippines only
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First recorded by Psst: 2 November 2026. Your first 3 jobs are free to apply to (the first right away, 2 more with your email). After that, the company and the apply link are for Psst members, from $4.99 for 7 days.
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The 20-second read
- Experience
- 3+ years required
- Where you can live
- Philippines only
- Skills and tools
- python
- Job type
- Full-time
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Job details
- Where you can work
- Philippines
- Employment type
- Full-time
- Advertised pay
- Not stated by the employer
- Experience
- 3+ years required
- Employer publication date
- 20 July 2026
- First recorded by Psst
- 2 November 2026
- Last checked at the source
- 10 November 2026
About the job
Role Summary
The company Outsourcing is looking for an LLM Engineer to design, fine-tune, and deploy large language model solutions that solve real business problems. You'll work across the model lifecycle — from prompt and architecture design to evaluation and production monitoring — collaborating with product and engineering teams to ship reliable AI features.
Key Responsibilities
- Design, fine-tune, and optimize large language models for specific use cases
- Build and maintain evaluation harnesses to measure model quality and safety
- Implement RAG (retrieval-augmented generation) pipelines and prompt strategies
- Monitor model performance in production and iterate on improvements
- Collaborate with product and engineering teams to integrate LLM features
- Stay current with emerging LLM research, architectures, and tooling
Requirements
- 3+ years of hands-on experience with large language models and NLP
- Strong understanding of transformer architectures (GPT, BERT, LLaMA, etc.)
- Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
- Experience with fine-tuning, prompt engineering, and hyperparameter optimization
- Track record of deploying ML/LLM systems in production environments
Nice-to-Haves
- Experience with vector databases and RAG pipelines
- Familiarity with model quantization and inference optimization
- Background in AI safety or content moderation systems
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