A Machine Learning Engineer’s Journey in Women in ICT
- Laura Gavrilut
- 20 minutes ago
- 3 min read
Please share a short biography
I am a Machine Learning Engineer based in Greece with a strong academic background in Data Science, Machine Learning, and Electrical & Computer Engineering. I studied at the National Technical University of Athens and the Technical University of Crete. Currently, I work at Athens Technology Center, where I design and deploy AI systems using Large Language Models (LLMs) and generative AI. My work focuses on solving real-world problems across sectors like fintech, media, and public services.
Please provide a short overview of your job and its relation to STEM. What is your current job? What is the field about?
As a Machine Learning Engineer, I develop intelligent systems that can learn from data and make predictions or decisions. My work is deeply rooted in STEM, combining computer science, mathematics, and data analysis. I use tools such as Python, PyTorch, Spark, and Hadoop to build and deploy scalable AI models that support decision-making in sectors like finance, telecommunications, and public services.
Who or what inspired you to follow this career path / start this job?
My curiosity about how machines can mimic human intelligence and solve complex problems led me to this field. Participating in competitions like the Google Hash Code and European BEST Engineering Competition further fuelled my interest in real-world data challenges and innovative solutions.
What does your typical working day look like?
My day typically involves reading the latest AI research, writing and testing machine learning models, collaborating with backend engineers, and preparing documentation. I work/have worked with large datasets and deploy models that support financial scoring or chatbot systems using LLMs.
Please give an overview of your study path and how you got into this career. If you could start all over again, how you would change the career path? Has there been an educational experience (formal/informal/traineeship/...) that helped you? Did your colleagues follow similar study and career paths?
I began my studies in Electrical and Computer Engineering at the Technical University of Crete, where I developed a solid technical foundation. I later pursued a Master’s in Data Science and Machine Learning at the National Technical University of Athens. Key experiences, including internships and thesis projects on EEG signal analysis and stock forecasting, helped shape my career. If I could start over, I might focus earlier on AI and gain more experience with real-world data from the start.
What are the professional & personal key skills needed to do your job?
1. Problem-solving: I troubleshoot issues in data pipelines and ML models daily.
2. Teamwork: Collaborating with cross-functional teams is essential in every project.
3. Programming (Python, Scala): I develop ChatBots and fine-tuning ML/LLMs Models.
4. Critical thinking: Used for model evaluation and algorithm selection.
5. Communication: I present technical concepts to both technical and non-technical stakeholders.
6. Adaptability: Rapid changes in AI technologies require constant learning and adaptation.
What types of jobs & industry sectors can you work in, with your skills?
With my skills, I can work in a variety of sectors including finance, healthcare, retail, media, and public administration. Roles may include Data Scientist, AI Researcher, NLP Engineer, and Big Data Engineer.
What are the main challenges in your job?
The main challenges include handling large and messy datasets, staying updated with rapid AI advancements, and ensuring the ethical use of AI. Ensuring models are scalable, reliable, and fair is an ongoing effort.
What is your advice to students?
Be curious, explore different areas of STEM, and don’t be afraid of challenges. Build projects, participate in hackathons, and learn how to work with data. Focus on developing both technical and communication skills.
How can teachers and parents support their students / children?
Encourage students to ask questions, experiment with technology, and learn programming early. Support their interests even if they seem niche—they may lead to innovative careers.
Please share any information to your career profile.