My Journey as a Senior Software Engineer in the Era of AI

When I began my career in software engineering, the industry was already moving quickly. But nothing compares to the velocity of change we’re experiencing now. New frameworks emerge seemingly overnight, established technologies evolve at breakneck speed, and the skills that were cutting-edge yesterday can feel outdated tomorrow.

As a Senior Software Engineer, I’ve had the privilege of working across logistics, travel, insurance, and SaaS industries, designing scalable platforms and integrating cutting-edge AI into production workflows . Each role has reinforced a truth that I wish I’d fully understood earlier: formal education is only the beginning.

From Classroom to Reality

My Bachelor’s in Information Technologies gave me a strong foundation—data structures, algorithms, networking, and the principles of good software design. But as thorough as it was, formal education can’t keep pace with the constant churn of technology. No degree program can cover every modern cloud service, evolving API standard, or AI tool that didn’t even exist when the curriculum was written.

This isn’t a flaw—it’s the nature of the field. The real work begins after graduation, and your learning never stops.

Books, Courses, and Traditional Learning

Early in my career, I relied heavily on programming books, best practices guides, and online courses. Resources like Clean Code, documentation deep-dives, and structured training platforms helped me grow my skills in frameworks like Django, React, and FastAPI.

Even today, these resources remain valuable—especially when you need a solid, time-tested understanding of a language, design pattern, or architectural principle. But the way I approach learning has shifted dramatically in recent years.

Enter the Era of AI and LLMs

The arrival of large language models (LLMs) like GPT, Claude, and others has completely transformed how I learn and work. Instead of searching through endless forum posts or skimming documentation for an obscure syntax detail, I can describe the problem to an AI and get a tailored, contextual response in seconds.

When I integrated AI tools into logistics workflows at Upwell —automating processes for bills of lading, proof of delivery, and freight invoices—I realized these same tools could enhance my own development process. Using LLMs, I can explore new frameworks faster, validate architectural choices, and even generate prototype code that accelerates feature delivery.

Adapting Workflows for the AI Age

AI-assisted development environments like Cursor and GitHub Copilot are not replacements for engineering expertise—they are multipliers. In my current work, Cursor helps me quickly iterate on features, generate test coverage, and even suggest performance optimizations.

The key is knowing when to lean on AI and when to rely on your own judgment. AI can draft a function in seconds, but as engineers, we ensure that it’s correct, secure, maintainable, and aligned with the broader system architecture.

The Path Forward

The pace of change in our industry isn’t slowing down—it’s accelerating. The engineers who will thrive are the ones who embrace continuous learning and adapt their workflows to leverage AI as a partner, not just a tool.

For me, this means blending the old with the new:

• Keeping a bookshelf of trusted programming references.

• Taking structured courses to deepen my understanding.

• Using LLMs and AI-driven IDEs to explore, validate, and accelerate my work.

Software engineering has always been about problem-solving. In the era of AI, we just have a much bigger toolbox. And for those willing to learn how to use it, the possibilities are endless.