Over 5 years of experience with a strong experience working with Python and machine learning technologies.
Design, develop, and maintain Java-based applications using Spring Framework and microservices architecture.
Need to have a deep understanding of how language models like GPT work, including their architecture, training methodology, and capabilities.
Need to have good knowledge of Tokenization: Tokenization is the process of breaking down text into smaller units (tokens) for analysis. Should be familiar with different tokenization techniques and their implications for prompt engineering.
Need to have good understanding of various Text Generation Techniques, including techniques for controlling the output of language models through prompts, prefixes, and conditioning.
Should have expertise in designing effective prompts tailored to specific tasks and objectives, since prompt engineering involves crafting input prompts or instructions to guide the language model's output.
Strong Fine-tuning Strategies skills, which involves adapting pre-trained language models to specific tasks or domains by providing task-specific data and fine-tuning parameters. Should know how to effectively fine-tune models for optimal performance.
Should be familiar with evaluation metrics commonly used to assess the quality and performance of text generation tasks, such as perplexity, BLEU score, ROUGE score, and human evaluation.
Should be familiar with Bias Mitigation, sincepotential biases present in language models. Should understand techniques for mitigating biases in model outputs, such as debiasing strategies and fairness-aware training.
Should have skills in interpreting and analyzing model outputs to understand how prompts influence the generated text and identify patterns or biases in the model's behavior.
Troubleshoot and debug issues, providing timely resolution to ensure smooth operation of applications.
Stay up-to-date with the latest industry trends and best practices in Java, Python, and machine learning.