About Rapid Global Growth
Join Rapid Global Growth (RGG), a dynamic international group, at our Dubai headquarters. As the technology and operations powerhouse behind Puffy - a Top 5 US mattress brand and one of North America's fastest-growing D2C companies - RGG drives innovation in global e-commerce. Since our launch in 2016, we've supported its remarkable 100% YoY growth, earning recognition from Forbes, Entrepreneur.com, and TechCompanyNews.
About the Role
We are seeking an exceptional NLP/LLM Research Engineer to join our team in Dubai. In this role, reporting directly to the Chief AI Operations Officer, you will architect and implement advanced language models that power our quantitative trading strategies. This is a unique opportunity to work at the intersection of LLMs and financial markets, where you'll develop sophisticated NLP solutions for real-time market analysis and social media sentiment processing. Your innovations will directly impact trading decisions and help shape the future of AI-driven financial technology.
Responsibilities
LLM Architecture & Optimization
- Design and implement multi-model LLM architectures for real-time sentiment analysis
- Optimize model performance for processing high-volume social media data
- Develop sophisticated prompt engineering strategies for improved accuracy
- Create robust validation frameworks for model outputs
- Implement efficient model deployment strategies for production environments
Sentiment Analysis Development
- Build advanced sentiment analysis systems for financial market events
- Develop classification systems for market-moving events
- Create sophisticated clustering algorithms for social media data
- Design systems for real-time sentiment scoring
- Implement context-aware sentiment analysis solutions
Model Validation & Testing
- Design comprehensive testing frameworks for LLM outputs
- Develop backtesting systems for sentiment analysis models
- Create validation protocols for historical data analysis
- Implement A/B testing frameworks for model comparison
- Build performance monitoring systems for production models
Research & Development
- Conduct research into the latest LLM developments and applications
- Explore innovative approaches to financial sentiment analysis
- Develop novel prompt engineering techniques
- Create new methodologies for market event classification
- Research efficient ways to handle real-time social media data
Required Skills
- 4+ years of relevant industry experience, with at least 2 years of experience in NLP/ML engineering and a minimum of 2 years of practical experience with modern LLMs.
- Advanced expertise in implementing and optimizing LLM architectures (proprietary and open-source models).
- Demonstrated mastery of prompt engineering and LLM fine-tuning techniques.
- Strong knowledge of LLM deployment, scaling, and multi-model architectures.
- Advanced proficiency in NLP techniques, sentiment analysis, and text classification systems.
- Expert-level Python programming with deep learning frameworks (PyTorch, TensorFlow).
- Proven experience with clustering algorithms and real-time text-processing systems.
- Strong command of SQL and database management for large-scale data operations.
- Extensive experience with cloud computing platforms and MLOps tools.
- Proficiency with version control systems (Git) and collaborative development workflows.
Beneficial Skills
- Experience with high-frequency trading systems or other real-time financial applications.
- Background in quantitative finance or algorithmic trading strategies.
- Previous experience working with market data and financial news sentiment analysis.
You Will Be Working On
- Developing improved classification systems for market events.
- Enhancing sentiment scoring accuracy for real-time tweets.
- Implementing efficient clustering for large-scale tweet analysis.
- Creating robust prompt engineering strategies.
- Building validation frameworks for historical data analysis.
- Work closely with trading strategists, MLOps and research team.
Performance Metrics
- Improvement in sentiment scoring accuracy.
- Reduction in false positives/negatives.
- Speed of market event classification.
- Accuracy of price impact predictions.