Solid foundation in algorithms, linear algebra, probability, and statistics.
Comprehensive knowledge of machine learning and deep learning concepts, including supervised, unsupervised, and reinforcement learning.
In-depth experience with natural language processing and large language models.
Familiarity with ML frameworks and tools such as numpy, pandas, scipy, scikit-learn, pytorch, and transformers.
English level B2+.
Responsibilities:
Conducting experiments with LLMs: Explore and analyze the effectiveness of different architectures and techniques to enhance the capabilities of AI models.
Developing and implementing evaluation methodologies: Implement and maintain robust frameworks to assess performance, accuracy, and user satisfaction of AI bots.
Optimizing models for inference: Improve the efficiency and speed of AI models during inference for production environments.
Collaborating with cross-functional teams: Work closely with data scientists, software engineers, and product managers to integrate AI solutions.