When people hear the words “Machine Learning Engineer,” they often picture PhD degrees, years of formal study, and deep mathematical expertise. My story is quite different, I don’t have a formal AI degree, yet today I’m working as an ML engineer. If you’re wondering whether it’s possible for you, I’m here to say yes, absolutely!

My Professional Background

I began my career in IT and later moved into DevOps. Working in this area for almost a decade grounded me in software engineering, automation, infrastructure, continuous integration and deployment (CI/CD), and most importantly, a deep love for problem solving. These skills turned out to be foundational for transitioning into machine learning.

My Turning Point

Like many, my interest sparked with the release of ChatGPT. Before ChatGPT, AI seemed distant and abstract to me. But interacting with ChatGPT changed everything. Witnessing firsthand what Generative AI could achieve made me an instant believer. Suddenly, I saw the future unfolding right in front of me, and I desperately wanted to be part of it.

How I Learned ML Without a Degree

Starting with Fundamentals

I jumped straight into online courses, beginning with Andrew Ng’s deeplearning.ai specialisation. These courses helped demystify complex topics, providing a strong foundation in neural networks, deep learning, and practical machine learning concepts.

Combining DevOps and ML

Leveraging my DevOps experience, I quickly identified the intersection of my existing skills with machine learning—MLOps. I studied our company’s internal ML team’s platform, spotted areas for improvement, and collaborated closely with them. Eventually, I delivered a tech talk at our internal conference, showcasing the ML platform and highlighting how traditional DevOps could transform ML workflows.

Hands-on Experience and Hackathons

Learning ML became hands-on when I participated in an internal hackathon, building a Retrieval-Augmented Generation (RAG) chatbot. Not only was it challenging and exciting, but our bot won the hackathon! This reinforced my belief that practical projects accelerate learning and build credibility.

Learning through Building and Sharing

Beyond structured courses, I embraced practical advices from fast.ai and developed numerous projects. I consistently shared these projects through demos, internal presentations, and blog posts. Writing publicly and participating in podcasts about AI helped me consolidate my learning and built my credibility in the ML community.

Taking the Leap

An opportunity arose internally after a while—a short-term secondment with one of our ML teams. Despite the risk and uncertainty around job security after the secondment, I decided to seize the opportunity. Thankfully, my managers were incredibly supportive. This experience was instrumental in helping me transition fully into ML.

The Next Opportunity

Just three months later, another internal opportunity appeared—a permanent role in a dedicated ML team. I applied and was thrilled to join this fantastic group of engineers. Now, my daily work involves practical ML projects, playing with cutting-edge Generative AI, and building reliable, scalable ML systems that directly serve millions of users. The combination of my newly acquired ML skills and strong engineering foundation empowered me to contribute meaningfully from day one.

If You Are Thinking The Same

  • Leverage your existing skills: Your current expertise is valuable. Identify overlaps (like DevOps with MLOps) to accelerate your transition.
  • Learn publicly: Blog about your journey, share your projects, speak at meetups or internal conferences. This builds credibility and deepens your understanding.
  • Embrace opportunities: Don’t shy away from internal opportunities, even if they’re uncertain or temporary. These are invaluable stepping stones.
  • Keep building: Practical projects reinforce theoretical knowledge. Keep developing, showcasing, and iterating.

Conclusion

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If you’ve ever doubted whether you could transition into Machine Learning without a formal degree—let my journey be proof that you can. Stay curious, leverage your existing expertise, embrace practical projects, and seize opportunities when they appear. Your path into ML is waiting for you to take your first step!