🚀 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 | 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗔𝗜/𝗔𝗜𝗢𝗽𝘀 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 I am Data Scientist. With a strong technical background, I am currently specializing in AIOps. 👩‍💻 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝘁𝗮𝗰𝗸

𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲: I have worked on various time series analyses and classification tasks. My main focus is on NLP and advanced use of Large Language Models (LLMs) with RAG and fine-tuning techniques. 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀: Following a bottom-up approach, I laid a solid foundation by studying low-level languages (C) and then moved on to Java, Typescript, and Python. 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝗶𝗲𝘀: I am a strong advocate of Test-Driven Development (TDD) and the application of SOLID and DRY principles. I also enjoy studying and applying design patterns wherever possible. 𝗖𝗹𝗼𝘂𝗱 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀: I have experience with both AWS (especially for developing serverless microservices using Lambda, SQS queues, and API Gateway) and Azure (mainly OpenAI and Document Intelligence).

🔍 𝗖𝘂𝗿𝗿𝗲𝗻𝘁𝗹𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴:

𝗔𝗜: AI is such a vast field that I can never fully explore it. Currently, I am focusing on various papers that illustrate the progress made in recent months with LLMs. 𝗔𝗜𝗢𝗽𝘀: This is the area of AI that I am most passionate about and where I feel I am most suited. I am acquiring specific certifications and strengthening my skills in Infrastructure as Code (IaC) and Continuous Integration/Continuous Deployment (CI/CD).

💡 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗣𝗵𝗶𝗹𝗼𝘀𝗼𝗽𝗵𝘆:

I firmly believe in the importance of an agile and "production-first" approach, even for AI projects. By applying agile methodologies, I promote the rapid development and release of functional MVPs to accelerate stakeholder feedback, clarify misunderstandings that often lead AI projects to fail prematurely, and refine products through continuous iterations. My mission focuses on bridging the gap between the development of AI models and their operational implementation, emphasizing feedback loops and retraining.