LLM Back-end Developer
Textie AI
Job Overview
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Job Description
LLM Back-end Developer at Textie AI
Textie AI is an innovative AI platform developed from Czech Republic. We're building cutting-edge language model solutions and need an expert to take our capabilities to the next level.
The Role
You'll be our LLM Back-end Developer, focused on understanding, optimizing, and training large language models to achieve the best possible results. This means diving deep into model architectures, experimenting with training techniques, fine-tuning approaches, and figuring out how to get maximum performance from our AI systems. You'll work on real problems, test new methods, and directly shape how our platform performs.
What You Need
- Strong understanding of LLM architecture and training processes.
- Hands-on experience with model fine-tuning and optimization.
- Practical knowledge of frameworks like PyTorch or TensorFlow.
- Understanding of prompt engineering, RAG systems, and how to evaluate model performance.
- Essential programming skills in Python.
- Comfortable working with large datasets and computational resources.
- Curiosity and the drive to stay on top of rapidly evolving AI research.
The Offer
- Opportunity to work on real-world LLM applications.
- Access to computational resources and latest tools.
- Collaborative environment with direct impact on product development.
- Competitive salary.
- Flexibility in how you work.
Key skills/competency
- LLM Architecture
- Model Optimization
- PyTorch
- TensorFlow
- Python Programming
- Prompt Engineering
- RAG Systems
- Model Fine-tuning
- AI Research
- Data Processing
How to Get Hired at Textie AI
- Research Textie AI's culture: Study their mission, values, recent news, and employee testimonials on LinkedIn and Glassdoor.
- Customize your resume: Highlight your LLM architecture, PyTorch/TensorFlow, Python, and RAG expertise for Textie AI.
- Showcase practical LLM projects: Prepare to discuss specific experiences in model fine-tuning and optimization.
- Understand Textie AI's product: Demonstrate curiosity about their AI platform and potential future enhancements.
- Prepare for technical and behavioral questions: Focus on problem-solving, collaboration, and rapid learning in AI.
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