The Future of Quality Assurance: Building Essential AI Engineering Skills

The Future of Quality Assurance: Building Essential AI Engineering Skills

In recent years, artificial intelligence has transformed at an incredible rate, becoming a core component of how engineers build, test and improve technology solutions. At EPAM Bulgaria, this shift is a part of daily life, shaping everything from quality engineering and automation to AI-native product development.

As systems grow more complex, the processes used to ensure their quality must evolve alongside them. Aneliya Duhalova, Quality Engineering Manager at EPAM, sees this change firsthand. "AI has completely changed how we think about quality engineering," Aneliya says. "While we used to primarily work with predefined scenarios, today we adapt to systems that do not always produce the same result." 

This unpredictability means traditional functional testing is no longer enough. Today's engineers must evaluate data quality, analyze model behavior and observe how solutions perform in real-world situations. This requires a new set of AI engineering skills: more flexibility, deeper analysis and a creative approach to problem-solving. 

The Shift to Continuous Testing 

One of the most significant challenges in testing AI-based applications is the absence of absolute certainty. Unlike traditional software, AI models often operate in gray areas. 

"AI solutions do not always have a right or wrong answer," Aneliya explains. "That is the biggest challenge, because we have to evaluate whether the results are useful, accurate and safe." 

Engineers must also watch for risks like inaccurate outputs or biases that may be tied to the specific data used to train the model. Because AI systems learn and change, testing shifts from a one-time gatekeeping task to a continuous process of monitoring and improvement. 

Technology and Human Judgment

While AI can automate routine tasks and quickly analyze massive datasets, it is not a replacement for human expertise. Aneliya uses AI tools to save time, generate ideas and handle daily automation tasks, but when it comes to decisions that directly affect users or business outcomes, she relies on her team. 

"When dealing with decisions that have a real impact on users or the business, human judgment takes the lead," she says. "The best results come from combining AI with a real engineer's experience." 

This approach is critical for EPAM’s enterprise clients. Global brands rely on EPAM to deliver scalable, secure innovations. When engineering teams combine AI-driven efficiency with rigorous human oversight, they accelerate time-to-market while protecting the client's brand reputation and user trust. 

This dynamic is reshaping the baseline expectations for technical talent. Engineers are now expected to think broadly about a system's architecture, as well as the data it consumes and the implications of its outputs. Technical skills remain vital, but soft skills are becoming true differentiators. For example, Aneliya looks for candidates who are highly adaptable, naturally curious and equipped with the critical thinking required to question AI results rather than simply accepting them. 

Building Future-Ready Skill Sets  

To keep pace in a rapidly changing environment, the EPAM Bulgaria team focuses on continuous, hands-on learning.  

"We rely on learning by doing — experimenting with new tools, working on real projects and regularly sharing knowledge within the team," Aneliya says. 

EPAMers build their foundations through certifications in generative AI and AI testing, combined with internal knowledge-sharing that focuses on emerging tech topics. They also have access to a robust global learning ecosystem that includes internal upskilling programs, hackathons and customized development plans. Because AI technologies shift month to month, EPAM prioritizes a culture where continuous education is built into the standard workday, rather than treated as an afterthought. This environment empowers engineers to test new ideas and expand their capabilities.

For those looking to build a career in AI engineering or quality engineering, Aneliya offers a straightforward approach. 

"Be curious and start experimenting right now," she says. "You do not need to know everything; it’s more important to understand how things work and be able to evaluate the results. Above all, do not be afraid to try — that is the fastest way to grow." She explains how this could look like learning basic prompt engineering, experimenting with open-source large language models (LLMs) or studying how data structures impact machine learning outputs.  

Today, adaptability and a critical mindset are just as crucial as writing great code. For engineers ready to embrace this shift, the opportunity to build solutions with real-world impact is just beginning.  

Interested in joining our team? Find the perfect fit at https://careers.epam.com