Redefining Engineering Excellence: Amplifying Impact with Product Skills in the AI Era

In this article, Maryna Rybalko discusses how AI is redefining the software engineering landscape. She highlights the importance of product thinking and outlines key skills engineers should develop to thrive and lead in the era of AI-powered DevOps.

Redefining Engineering Excellence: How Product Skills Amplify Your Impact in the Era of AI

Author: Maryna Rybalko

Introduction

The rapid evolution of artificial intelligence (AI) is transforming the landscape of multiple industries, not least software development. The question confronting engineers isn’t whether AI will change their roles—this has already happened. The real question: How can engineers amplify their impact and thrive in this new reality?

Drawing from over five years of experience in software engineering—including roles as tech lead, engineering manager, and platform engineering lead at a major global job aggregator—Rybalko shares observations on the future of software development. AI now allows engineers to delegate repetitive and routine tasks, effectively functioning as a ‘personal junior engineer.’ This opportunity prompts a vital question: What is next for software engineers?

First Principles: Understanding the Engineer’s Evolving Role

Applying first principles thinking—a method of breaking down complex problems to their basics—Rybalko uses the ‘Five Whys’ technique to analyze how AI enhances, rather than threatens, software engineers’ roles.

1. Why do engineers write code? To build new features.
2. Why build new features? To solve problems or meet user needs.
3. Why solve problems or meet user needs? To create value for the business and improve user experience (UX).
4. Why create value for the business? To ensure company growth and sustainability.
5. Why is company growth and sustainability important? It provides jobs, drives innovation, and contributes to the economy.

A ‘sixth why’—drawing on Steve Jobs—asks why innovation matters at all. More than profitability, it’s about leaving a meaningful mark and contributing something valuable to the world.

Conclusion: Software engineering centers on value creation and innovation. This strategic focus requires human insight and creativity—core areas where AI cannot replace people.

The Shift: From Software Engineer to Product Engineer

Traditionally, software engineers focused on clean, efficient code. Now, AI shifts emphasis toward strategy and problem-solving. Engineers who embody ‘product thinking’—combining technical and business skills—are indispensable.

Product engineers bridge:

• Product strategy and OKRs
• User understanding and UX design
• Product strategy involvement
• Development of a product mindset

Why Product Thinking is Essential in the AI Era

AI serves not just as an automation tool for repetitive tasks—it’s reshaping how products are built, tested, and deployed. As AI models generate code, optimize infrastructure, and predict software failures, engineers must redefine their roles.

Key shifts:

• From Doers to Thinkers: AI can generate code but can’t fully grasp user pain points, business goals, or scalability challenges.
• Cross-Functional Innovation: AI automation frees engineers to drive innovation at the intersection of tech, business, and UX.
• Designing with AI in Mind: Top engineers design workflows, privacy standards, and ethics for an AI-rich future.

Key Product Skills for Engineers

To stay ahead, engineers must develop certain product skills:

1. Understanding Product Strategy and OKRs:
   • Engineers are expected to deliver value, directly linked to company revenue and customer satisfaction.
   • Engage with product teams; seek to understand short- and long-term company goals and strategies.
   • E.g., if an OKR is to increase e-commerce sales by 15%, engineers should focus on features like streamlining checkout processes to achieve this.
2. User-Centric Thinking:
   • Engineers must understand users deeply to solve the right problems.
   • Use frameworks like Jobs To Be Done and Customer Journey Mapping.
   • Combine qualitative feedback (user insights on pain points) and quantitative data (tracking friction points in user flows) to identify and validate issues.
3. Developing Product Solutions:
   • Focus on hypothesis-driven development. Frame ideas as hypotheses and validate via experiments or A/B testing, using frameworks like DIBB (Data, Insight, Belief, Bet).
   • Demonstrates strategic, structured proposal skills aligning technical work with business priorities.
4. Product Mindset:
   • Constantly evaluate if what is being built is genuinely valuable.
   • Adopt a proactive, investor-oriented attitude to features and solutions.
   • Dig into customer data, remain curious, drive ideas and improvement initiatives.

The Future of Engineering

Engineers are uniquely positioned to lead in the era of AI. Rybalko advises:

• Take initiative
• Secure buy-in
• Drive the product forward
• Think differently

Ultimately, engineering excellence in the AI era depends on the ability to combine technical skills with product strategy and insight.

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