AI is reworking industries at an unprecedented tempo, and navigating this evolving panorama requires each technical experience and strategic imaginative and prescient. On this interview, we converse with Maryna Bautina, Senior AI Guide at SoftServe, who brings intensive expertise in machine studying, AI-driven enterprise options, and management. Maryna shares insights on bridging software program engineering with AI, scaling into management roles, overcoming MLOps challenges, and aligning AI innovation with enterprise objectives. She additionally discusses {industry} traits and presents profession recommendation for professionals seeking to advance in AI.
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How has your software program engineering diploma influenced your method to machine studying?
It taught me the right way to write clear, scalable code, construction advanced methods, and suppose when it comes to efficiency and maintainability – all important for constructing real-world AI options. As a substitute of simply specializing in mannequin accuracy, I method ML with an engineering mindset, guaranteeing that fashions are environment friendly, reproducible, and deployable in manufacturing. Having a robust basis in knowledge buildings, algorithms, and software program structure additionally helps me optimize ML pipelines, deal with large-scale knowledge effectively, and combine fashions into current methods, guaranteeing that fashions don’t simply work in a pocket book however may be monitored, retrained, and scaled successfully. It helps me bridge the hole between analysis and real-world AI functions, guaranteeing that machine studying options are usually not solely correct but in addition sensible and strong.
Are you able to stroll us by way of your expertise rising from a mid-level function to a management place at SoftServe? What had been a few of the greatest challenges you confronted on this journey?
Fairly early on, I noticed that technical expertise alone weren’t sufficient – I wanted to know the larger image, like how AI suits into enterprise technique and the right way to work successfully with totally different groups. As I took on extra duties, I had the chance to step right into a technical management function. This meant not simply fixing issues myself however guiding a staff, guaranteeing high-quality work, and ensuring our AI options aligned with enterprise wants. Working in a consultancy setting was a fantastic studying expertise as a result of I acquired publicity to totally different industries, which helped me see how AI can drive worth in varied contexts. One of many greatest challenges was maintaining with the fast-moving AI panorama. There’s all the time one thing new – whether or not it’s a breakthrough mannequin, a brand new framework, or shifting {industry} traits – so steady studying has develop into a necessity. One other problem was transitioning from being a person contributor to a pacesetter. It wasn’t nearly coding and problem-solving anymore; I had to consider staff dynamics, communication, and the strategic influence of our work. In the end, this journey helped me develop each technically and as a pacesetter, studying the right way to bridge the hole between AI innovation and real-world enterprise influence.
What does being a Senior Member of IEEE imply to you, and the way has it contributed to your skilled development in AI and machine studying?
It’s each an honor and an effective way to remain related with the AI and engineering neighborhood. It’s not only a title—it displays the work I’ve carried out within the subject and offers me entry to a community of high professionals, cutting-edge analysis, and {industry} discussions. One of many greatest advantages is staying up to date on the newest traits in AI, machine studying, and Generative AI. By means of IEEE, I get entry to technical conferences, analysis papers, and collaborations that assist me continue to learn and rising. It additionally provides me an opportunity to contribute – whether or not it’s sharing insights, discussing greatest practices, or serving to form accountable AI requirements. Past the technical facet, being a part of IEEE has been an effective way to satisfy like-minded professionals, change concepts, and keep concerned in conversations that form the way forward for AI. It’s a relentless reminder of the significance of studying, sharing information, and driving AI developments in a accountable approach.
How do you steadiness technical innovation with aligning AI and machine studying options to satisfy enterprise objectives?
I attempt to preserve the steadiness by specializing in three principal issues: preserving AI tasks tied to actual enterprise wants, experimenting responsibly, and dealing intently with totally different groups. First, AI ought to all the time resolve an actual downside – it’s not about utilizing the newest tech simply because it’s thrilling. I be certain that each AI initiative is tied to measurable enterprise outcomes, so it really drives influence somewhat than simply being a cool experiment. Second, whereas staying on high of latest developments is vital, not each cutting-edge concept is sensible. I all the time consider issues like scalability, knowledge availability, and cost-effectiveness earlier than diving in. It’s about discovering the correct mix of innovation and real-world feasibility. Lastly, AI isn’t inbuilt isolation. I work intently with enterprise leaders, product managers, and area specialists to ensure AI options match seamlessly into enterprise processes. It’s this collaboration that turns AI from a technical achievement into one thing that delivers actual worth.
With over seven years of expertise throughout varied industries, what’s one of the impactful AI-driven tasks you’ve labored on, and what outcomes did it ship?
If I had to decide on one, it could be growing an AI-powered conversational analytics system that extracted insights from buyer help interactions for a global monetary establishment. They struggled to investigate huge quantities of unstructured dialog knowledge, making it tough to establish recurring points, buyer ache factors, and alternatives for product enchancment. Our answer leveraged giant language fashions to routinely extract key components comparable to downside statements, troubleshooting steps, decision methods, and product references. The system detected traits, enabling the corporate to proactively handle frequent points and improve the shopper expertise. Moreover, it generated summarized stories and information base entries, considerably decreasing guide evaluate time whereas enhancing decision effectivity. The AI-driven system minimize evaluate time by 80% and elevated decision effectivity, permitting help groups to work extra successfully whereas serving to companies optimize their merchandise primarily based on actual buyer suggestions. This undertaking demonstrated the ability of Generative AI in reworking enterprise information administration, proving that AI can do greater than automate – it might probably generate strategic worth by turning unstructured knowledge into actionable intelligence.
As a pacesetter in knowledge science, how do you method managing groups with various technical talent units, and what methods do you utilize to foster collaboration and innovation?
Managing a staff comes down to a few issues: taking part in to strengths, sharing information, and fostering a problem-solving mindset. First, I be certain that everyone seems to be engaged on duties that match their experience whereas additionally giving them probabilities to develop. Extra skilled staff members deal with advanced challenges, whereas these nonetheless studying get hands-on expertise with the appropriate help. Second, I encourage open information sharing – whether or not by way of casual mentorship, staff discussions, or working collectively on tasks. Nobody ought to really feel like they’re fixing issues alone, and the very best concepts typically come from bouncing ideas off one another. Lastly, I attempt to create an setting the place experimentation is welcome and various views are valued. AI is all about fixing real-world issues, so I be certain that brainstorming is sensible and centered on significant influence. This method retains the staff engaged, helps everybody develop, and results in stronger, simpler AI options.
MLOps is gaining important traction within the {industry} – what are a few of the greatest challenges you face when implementing MLOps practices, and the way do you overcome them?
Implementing MLOps isn’t all the time easy crusing – it comes with challenges like scaling, automation, reproducibility, and getting totally different groups on the identical web page. One of many greatest complications is integrating it into current methods, particularly when corporations have a mixture of cloud, on-prem, and legacy infrastructure. To deal with this, we give attention to standardizing workflows, utilizing containerization (like Docker), and selecting cloud-agnostic instruments that make deployments extra versatile. One other problem is automating the ML lifecycle whereas preserving fashions dependable in manufacturing. Issues like CI/CD for ML, knowledge drift, and monitoring mannequin efficiency can get tough. We handle this by utilizing characteristic shops, organising automated retraining pipelines, and implementing monitoring instruments to catch points early. Lastly, MLOps requires a tradition shift – knowledge scientists, DevOps, and enterprise groups must work collectively extra intently and undertake software program engineering greatest practices in ML growth. To bridge this hole, we use model management for fashions and datasets (like DVC or MLflow), hold documentation clear, and ensure there are common cross-team check-ins. On the finish of the day, the important thing to overcoming MLOps challenges is a mixture of the appropriate instruments, automation, and robust collaboration between groups.
How do you foresee AI and automation persevering with to form enterprise operations within the subsequent 5 years, and the way can professionals in AI keep forward of the curve?
AI and automation are going to maintain reworking enterprise operations in large methods over the following 5 years. We’ll see extra hyper-personalization, real-time decision-making, and automation at scale. Generative AI, AI-powered analytics, and autonomous methods will develop into much more frequent, serving to companies optimize workflows, enhance buyer experiences, and create new income alternatives. AI copilots will seemingly be normal instruments throughout industries, aiding professionals with advanced duties, whereas automated decision-making will streamline areas like finance, provide chain, and buyer help. Plus, with developments in multimodal AI and edge computing, AI will be capable to function extra effectively in real-world settings, decreasing delays and enhancing general efficiency. For AI professionals, staying forward means consistently studying. Rising applied sciences like LLMs, reinforcement studying, and AI ethics are evolving quick, so maintaining with traits is essential. Arms-on expertise with open-source AI fashions, cloud platforms, and real-world functions will probably be important for staying aggressive on this ever-changing panorama.
Along with your huge expertise throughout sectors like retail, schooling, and e-commerce, what industry-specific AI traits or challenges do you discover most intriguing proper now?
Probably the most thrilling AI traits proper now could be how Generative AI is driving hyper-personalization and automation throughout totally different industries. However every sector has its personal distinctive challenges. In retail, AI is making demand forecasting, dynamic pricing, and customized suggestions extra correct. The tough half is maintaining with consistently altering client conduct whereas additionally respecting privateness considerations. In schooling, AI-powered adaptive studying and automatic content material creation are making studying extra partaking. Nonetheless, ensuring AI-generated content material is correct, truthful, and aligned with correct educating strategies is a giant problem. In e-commerce, AI is enhancing buyer expertise by way of chatbots, smarter search, and automatic success. However points like pretend opinions, algorithmic bias, and rules round AI-generated content material have gotten larger considerations. Throughout all industries, companies must give attention to scalability, AI governance, and accountable AI adoption. Corporations that combine explainable AI, real-time analytics, and moral AI practices won’t solely keep aggressive but in addition construct stronger buyer belief.
What’s your philosophy on management within the knowledge science house, and the way do you make sure that your staff’s work stays aligned with long-term strategic objectives?
It’s all about preserving issues sensible, outcome-driven, and aligned with actual enterprise wants. I be certain that my staff’s work stays on monitor by sustaining clear priorities, encouraging open communication, and guaranteeing that our tasks instantly contribute to long-term objectives. Since enterprise wants can change shortly – particularly in response to buyer calls for – I construct flexibility into our workflow. This manner, we will adapt with out shedding momentum. Reasonably than chasing AI traits only for the sake of innovation, I push for options which might be each impactful and scalable. To maintain tasks transferring in the appropriate route, I set clear milestones, encourage iterative growth, and create suggestions loops so we will shortly alter as wanted. I additionally consider in hands-on collaboration – everybody ought to have the help they should develop whereas staying centered on delivering actual worth. On the finish of the day, it’s all about balancing technical excellence with adaptability and enterprise relevance. That’s how we be certain that our work isn’t simply revolutionary but in addition drives significant, lasting outcomes.
Lastly, for professionals seeking to advance their careers in AI, what recommendation would you give them concerning talent growth, profession trajectory, and staying aggressive in an ever-evolving subject?
If you wish to develop your profession in AI, my greatest recommendation is to give attention to three issues: mastering the appropriate expertise, staying adaptable, and constructing a robust community. First, go deep into core AI/ML expertise like deep studying, generative AI, MLOps, and knowledge engineering – however don’t simply cease at concept. Get hands-on expertise with real-world tasks that contain all the lifecycle, from constructing fashions to deploying and monitoring them. AI isn’t nearly coaching fashions; it’s about making them work in manufacturing. Second, AI is transferring quick, so staying adaptable is essential. Sustain with the newest analysis, open-source instruments, and {industry} traits. Interact with the AI neighborhood – whether or not it’s by way of conferences, on-line boards, or contributing to open-source tasks. Don’t be afraid to discover new areas like multimodal AI, reinforcement studying, or AI ethics to increase your experience. Lastly, technical expertise alone received’t get you forward. The very best AI professionals know the right way to talk their work’s enterprise influence, collaborate with totally different groups, and suppose strategically. Writing blogs, giving talks, or mentoring others may also assist place you as a thought chief. By combining robust technical expertise with strategic pondering, communication, and steady studying, you’ll keep aggressive and set your self up for management alternatives in AI.
