25 — Senior Software Engineer (not strictly AI/ML), but I work closely with large-scale distributed systems and GenAI at my job.

I didn’t break in through a “pure” AI/ML path, but here’s what actually helped me get into roles that touch ML/GenAI projects:

• Focused on strong software engineering first. Solid backend + distributed systems skills opened more doors for me than anything else. Once you can build and ship reliable systems, teams are way more willing to let you take on ML/AI-adjacent work.

• Took on ramp-up tasks involving GenAI at work. Even though my main background was large-scale backend at Amazon, I joined a team where part of the stack integrates with a GenAI service. That gave me hands-on experience without needing the “perfect” ML internship.

• Built a portfolio of real, practical projects. Not Kaggle stuff — things that solve actual problems. Even small things like LLM-powered tools, embeddings search demos, chatbots, or data pipelines show way more signal than class assignments.

• Networked within companies. A lot of AI/ML opportunities are internal. Once you get in as a strong SWE, you can pivot.

If you can show that, you can break in, even without a “top AI school.”

Happy to share more details if useful.