NLP/LLM Research Engineer, Rapid Global Growth

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

Requirements & 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