Conducting experiments with LLMs: Explore and analyze the effectiveness of different architectures and techniques (SFT, RLHF, Adapters, etc.) to enhance the capabilities of AI models;
Developing and implementing evaluation methodologies: Implement and maintain robust frameworks to assess the performance, accuracy, and user satisfaction of AI bots, including offline and online metrics;
Optimizing models for inference: Improve the efficiency and speed of AI models during inference to ensure they meet the performance and scalability requirements for production environments;
Collaborating with cross-functional teams: Work closely with data scientists, software engineers, and product managers to integrate AI solutions into the overall product pipeline.
Requirements & Skills:
Proficiency in Python, algorithms, and mathematics: Strong coding skills in Python and a solid foundation in algorithms, linear algebra, probability, and statistics;
Deep understanding of ML and DL principles: Comprehensive knowledge of machine learning and deep learning concepts, including supervised, unsupervised, and reinforcement learning;
Comprehensive expertise in NLP and LLM: In-depth experience with natural language processing and large language models;
Familiarity with ML frameworks and tools: Numpy, pandas, scipy, scikit-learn, PyTorch, transformers;
