Introduction
I’m Cristian Sarmiento, a Full Stack Engineer with over a decade of experience in software development, from building satellite simulators to designing scalable healthcare solutions. My career is a testament to solving real-world problems—complex, unpredictable challenges that demand hands-on ingenuity. I don’t have a degree; I learned my craft by doing, diving into projects and figuring things out as I went. This approach has fueled my success, but it’s also why technical interviews feel like an insurmountable hurdle. They’re built for people who excel at memorizing algorithms and reciting theory, not for someone like me who thrives on practical problem-solving.
As the tech industry evolves—especially with AI reshaping our roles—it’s time to rethink how we evaluate talent. In this article, I’ll share my journey, explore the science behind my learning style, and propose changes to the interview process that align with the future of tech and the human skills that matter most.
My Journey: A Decade of Hands-On Innovation
My career spans a diverse range of roles and industries, all unified by a passion for building solutions that work. At CareJourney by Arcadia, I led a team to develop serverless APIs and micro frontends using Nest.js and React, delivering secure, scalable healthcare solutions. I managed AWS infrastructure to ensure compliance and collaborated with cross-functional teams to integrate technologies seamlessly, improving efficiency and user experience.
Before that, at Darwoft, I created a QA automation area from scratch, rooted in an architectural principle of integration. What started as a solo effort grew into a 40-person team, with a client now relying on the architecture I designed. I also worked as a solo Python developer for a startup client, building Django applications, designing databases (Postgres, DynamoDB, Elasticsearch), and using JIRA APIs to train models that boosted team efficiency.
At INVAP, I spent years on satellite projects like SAOCOM 1A, 1B, ARSAT-1, and ARSAT-2. I developed C++ functionalities for the SAOCOM Mission Simulator, created a Python + Qt GUI API, and managed flight operations for power and thermal subsystems. My work ensured these satellites performed optimally in orbit—real stakes, real results.
Earlier roles at CDSI, Nimbuzz, and Globant honed my skills in quality assurance and testing across platforms, from mobile games to messaging apps. Each project taught me something new, not through textbooks, but through trial, error, and iteration.
These experiences showcase my ability to tackle complex challenges and deliver tangible outcomes. Yet, despite this track record, technical interviews remain a stumbling block.
The Struggle with Technical Interviews
Technical interviews don’t reflect how I work. I’ve stood at whiteboards, asked to implement a binary search tree or explain an algorithm under pressure, and felt my mind lock up. It’s not that I don’t know these concepts—I’ve applied them in real projects for years. But I learned them by using them, not by memorizing them in isolation. My strength lies in building systems, debugging failures, and collaborating on solutions, not in performing theoretical exercises on demand.
Traditional interviews reward quick recall and textbook fluency—skills that don’t always translate to the job. For someone like me, who learns through experience, this setup highlights my weaknesses rather than my strengths. It’s frustrating to know I can design architecture for a 40-person team or keep a satellite in orbit, yet struggle to prove my worth in a 60-minute coding quiz.
The Science Behind Learning by Doing
My learning style isn’t a fluke—it’s backed by science. Constructivist learning theory, pioneered by Jean Piaget, argues that we construct knowledge through active engagement with our environment. It’s not about absorbing facts; it’s about building understanding through experience. That’s how I’ve approached every project—diving in, testing ideas, and refining solutions.
David Kolb’s experiential learning model takes this further, positing that knowledge emerges from transforming experience into practical insights. Research supports this: studies on project-based learning in STEM fields show that hands-on engagement leads to better retention and application of knowledge compared to traditional lectures. My 10 years of building systems—from satellites to software—mirror this process. I’ve learned more from debugging a failing API than I ever could from a classroom.
AI and the Shifting Industry Landscape
The tech world I entered a decade ago is unrecognizable today, thanks to AI. Routine tasks are being automated, and the focus is shifting to skills AI can’t replicate: critical thinking, creativity, and adaptability. The World Economic Forum’s Future of Jobs Report (2020) underscores this, listing these abilities as top priorities for the future workforce.
This shift aligns perfectly with my strengths. My career is built on adapting to new challenges—whether it’s integrating AI tools at Darwoft or optimizing satellite operations at INVAP. As AI handles the grunt work, engineers need to experiment, learn quickly, and pivot as technologies evolve. Memorizing algorithms matters less when you’re working alongside systems that adapt in real time. My hands-on experience feels more relevant than ever, yet the interview process hasn’t caught up.
Reimagining the Interview Process
To find talent suited for this AI-driven future, we need to overhaul technical interviews. Here’s how, based on my experience and the industry’s trajectory:
1. Replace Whiteboards with Real Projects
Ditch the on-the-spot coding tests. Give me a practical challenge—like building a small application or fixing a bug—and a few days to solve it. This mirrors real work and lets me showcase my ability to deliver results.
2. Focus on Problem-Solving, Not Memorization
Ask how I’d tackle a problem, not whether I can recite an algorithm. Let me walk through my process—how I analyze, experiment, and iterate. In an AI world, how I think matters more than what I’ve memorized.
3. Incorporate Pair Programming
Most of my work involves collaboration. A pair programming session would reveal how I code, communicate, and adapt in real time—without the artificial pressure of a solo test.
4. Evaluate Past Work
My projects—like the QA automation architecture at Darwoft or the SAOCOM simulator—say more than any interview could. A portfolio review or deep dive into my contributions would highlight my practical impact.
5. Test AI Adaptability
Ask how I’ve used AI tools or learned new technologies. Assess my ability to experiment and grow with the systems shaping our field. That’s a skill you can’t measure on a whiteboard.
These changes would better evaluate engineers like me, who excel in practice but falter in theory-heavy settings. They’d also uncover talent ready to thrive in a world where AI is a partner, not a replacement.
Conclusion
I’ve spent over 10 years proving that hands-on experience can outshine a degree. From satellites to healthcare software, I’ve built a career on solving real problems, even if technical interviews don’t always recognize it. Science validates my approach: learning by doing is powerful. And with AI transforming tech, the skills I’ve honed—adaptability, problem-solving, persistence—are more vital than ever.
It’s time for interviews to evolve. By prioritizing practical skills over theoretical drills, companies can tap into a broader talent pool and build teams equipped for the future. I’m not asking for a shortcut—just a chance to prove myself the way I always have: by getting my hands dirty and making things work.
References
- Piaget, J. (1950). The Psychology of Intelligence.
- Kolb, D. A. (1984). Experiential Learning: Experience as the Source of Learning and Development.
- Krajcik, J. S., & Blumenfeld, P. C. (2006). Project-based learning.
- World Economic Forum (2020). The Future of Jobs Report.
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