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As artificial intelligence continues to shape the future of business, new roles are emerging to unlock its potential. One of these exciting paths is the machine learning (ML) engineer – a professional who builds systems that help machines learn from data and make smarter decisions.
At Telia, Simonas Adomavičius is part of this change. He shares his journey into the world of data and AI, what inspired him to take this direction, and why working as an ML engineer is both dynamic and deeply meaningful.
Whether you’re just starting to explore the tech field or considering a shift into something new, Simonas’ story is a great example of where curiosity, learning, and courage can take you.
From Political science to ML engineering
Simonas’ path into the world of data wasn’t linear.
“I studied political science during my bachelor’s but ended up working in sales. After 2 years I’ve decided to change my career and went to digital marketing. Then I started to notice that the best time I’m having at work is when dealing with numbers or trying to automate something. That’s when I realized my passion lies in working with data” says Simonas.
Today, as an ML engineer at Telia, he works alongside data scientists and engineers to bring advanced data-driven solutions to life. His role is becoming increasingly relevant across the tech and telecommunications sectors.
“The dynamic nature of the market and current analytics trends show that machine learning algorithms can solve a wide range of business challenges,” Simonas explains. “They help us be first in offering smarter solutions to both customers and our own teams.”
No room for boredom in ML Engineering
The role of a machine learning (ML) engineer is broad and constantly evolving. Since the field is still relatively young in Lithuania, ML engineers often wear many hats — from maintaining models and preparing data to supporting virtual environments and building the infrastructure that powers machine learning development.
“My job combines many disciplines,” explains Simonas. “I maintain the models created by our data scientists and make sure they have the right tools to work quickly and effectively.”
Each month brings something new. Recently, Simonas worked on developing a monitoring system for ML models, as well as building a Kubernetes (K8s) template for a Python app that collects customer feedback on Telia Play recommendations. This reusable template helps speed up future development and makes similar tools easily accessible across the company.
What it takes to succeed in ML Engineering
Becoming a skilled ML engineer takes time, curiosity, and persistence. One of the biggest challenges? Navigating the ever-growing ocean of tools and technologies to find the right fit.
“Tech evolves faster than we can fully adapt,” says Simonas. “Even the best ML solution won’t succeed without a solid implementation strategy and collaboration across the team.”
At Telia, success in this role is shaped not just by technical skills, but also by soft skills — like a growth mindset, clear communication, and being a reliable team player – the field is complex, you need someone that you know will have your back.
“Being able to plan realistically, support a teammate who’s stuck, and stay curious about new trends — these things matter just as much as writing good code. We work in a culture of trust and support, where people genuinely want each other to succeed.”
Thinking about a career in ML Engineering? Here are 3 tips from Simonas:
- Start with a strong data foundation.
Learn the basics of data manipulation, Python, and SQL. Understanding how to work with data is the first step before diving into machine learning models. Because if you’ll have garbage in your ML solution, you’ll get garbage out as an output. - Don’t fear the learning curve — start small.
Learn the basics of DevOps, from there move into the field of MLOps, try to build automated ML pipelines. On the way you’ll be forced to learn about linux, containerization and other software engineering skills. You don’t need to become the master of all of those disciplines, only enough to get the final solution working. Making it performant is a step only after that. - Soft skills matter more than you think.
Communication, collaboration, and curiosity are essential. Being part of a team, supporting others, and continuously learning will take you far — even more than perfect code.
Looking for new career opportunities?
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