Position: Machine Learning Engineer
Category: Data Science
English level: fluent
Years of experience: 5


Projects and Tasks Completed:
I have had the privilege of working on a variety of projects over the past few years, encompassing a wide range of technologies and tasks:
1. Speech Recognition Project (2 years): This project was a comprehensive end-to-end endeavor. I began with data collection and analysis to understand the requirements and nuances of the task. I was involved in modeling, where I designed and implemented speech recognition models. Continuously, I tracked competitors and industry trends to ensure our project remained competitive. Model training and testing were pivotal aspects of my role, ensuring that our system performed optimally. For these parts I used SQL for data collection and queries, PyTorch and PyTorch-Lightning for trainings. I also dedicated significant effort to making the system noise-robust and accent-robust, enhancing its usability across diverse environments and accents.
2. NLP and text post-processing: In addition to speech recognition, I worked on various natural language processing (NLP) tasks. This included named entity recognition, word tagging, text summarization, and inverse text normalization. These tasks required a deep understanding of NLP techniques and tools, like LLMs, Bert, Language Modeling from scratch, etc.
3. Medical Audio Analysis (Thesis): During my Master's thesis at Technical University of Munich,  I conducted research in medical audio analysis. Specifically, I focused on generative technologies for audio data in a medical context. This work contributed to the broader field of healthcare technology.
4. Bio-Physiological Data and Cognitive Load Analysis: In my current project I'm working with bio-physiological data and I'm trying to model data and derive predictions from data provided by bio-sensors, such as stress, tiredness, heart rate, etc.
My current role in this team is being engaged in data analysis, problem formulation, modeling, coding and research tasks. I'm now working in a flat-surface team, which gives me both the freedom and responsibility of achieving my goals on my own, without being co-dependent of my colleagues, with that, of course, a team-driven environment is a huge concept of my work culture and I'm working with a team with a lot of excitement.

While I have gained valuable experience in various domains, I am always eager to continue learning and growing. Specifically, I am interested in latest advancements in ML and NLP and strengthening my leadership and management skills.