Over the last few years, Generative AI (GenAI) has gone from a research buzzword to a career-defining opportunity. From large enterprises to lean startups, everyone is exploring how to harness the power of Large Language Models (LLMs) to drive innovation. Naturally, the demand for developers who can build GenAI-powered applications is booming.
But how do you actually break into this space?
We sat down with one of our consultants currently working as a Generative AI Developer to learn about his path, mindset, and the hands-on strategies that helped him land the role. If you’re looking to move into this high-demand field, his experience might just give you the roadmap you need.
(Note: For confidentiality, we’ve kept the consultant’s identity anonymous.)
Q: How did you land your current role in Generative AI?
I started my career as a data scientist in India, working mostly with text classification and Natural Language Processing (NLP). I later pursued a master’s in Business Analytics in the U.S. During the program, I took a course on Large Language Models (LLMs), and that gave me a strong foundation in GenAI. Combined with my prior experience, this made it a natural transition.
Q: Was your academic background deeply technical?
Not exactly. My degree focused on applying analytics to business use cases, not pure engineering. But it included a hands-on course in LLM-based app development, which turned out to be extremely useful.
I’ve always believed that whether you’re an analyst or a developer, the end goal is to solve real business problems. That mindset helped me pivot into GenAI roles, even though my academic background wasn’t entirely technical.
Q: What helped the most when preparing for interviews?
Hands-on experience, without a doubt. I built small GenAI applications on my own, deployed them on cloud platforms like AWS and Azure, and learned by doing.
Most interviews test whether you can take a business problem, choose the right tools, and actually implement a working solution. So just watching videos won’t cut it—you need to get your hands dirty and build something.
Q: What skills do you think are essential for someone entering this field?
A few which absolutely stand out in today’s world are:
- Understanding business needs — Your models must solve real problems
- Communication — Be able to explain complex ideas to non-technical stakeholders
- Statistics & experimentation — These are core to data science
- Software development skills — Python, Kubernetes, databases, etc.
- Deployment knowledge — Knowing how to take your model live is a huge plus Even if deployment isn’t your responsibility, understanding the full development lifecycle is key.
Even if deployment isn’t your responsibility, understanding the full development lifecycle is key.
Q: How do you stay up to date in such a fast-moving field?
I rely on Reddit and X (Twitter). These platforms help me track the latest research, tools, and use cases. You’ll often find breaking developments there hours before they show up in traditional sources.
Follow researchers, engineers, and GenAI thought leaders—people who build these tools or write about them regularly.
Q: Any learning strategies you would recommend to newcomers?
Yes—build, break, and repeat. Pick a problem, build a solution, test it, and deploy it. Even something simple like a chatbot which answers queries for a food recipe can teach you a lot. Use the free tiers of AWS or Azure to simulate real-world deployment.
Don’t wait until you’re an “expert.” You’ll learn far more by trying to solve problems than by reading about them.
Q: What do you find most rewarding about working in Generative AI?
You’re constantly solving new real-world problems. There’s no set template because the technology is so new that no one has built it before. That makes the work incredibly creative and fulfilling.
It’s also rewarding to see something you’ve built being adopted by others. You know it’s not just code—it’s a solution that’s making an impact.
Q: What’s your one piece of advice to someone looking to become a GenAI developer?
Start building today. Don’t overthink it. Pick a problem, choose a tool, and start experimenting. Even if you fail, you’ll learn faster than you would from any course or tutorial.
Where to Start If You are New to GenAI
If this Q&A inspired you to explore a career in GenAI, here are a few steps you can take right now:
- Pick a problem you care about — and try to solve it with an LLM
- Learn by doing — tutorials help, but real learning happens when you build
- Create a GitHub repo or portfolio — even basic projects matter
- Join communities — Reddit, Discord, X (Twitter), and open-source forums
- Explore certifications — Google Cloud GenAI, DeepLearning.AI, or Prompt Engineering basics
Pro tip: You don’t need to know everything to get started. The field is still maturing, and companies are looking for learners, not just experts.
Explore Opportunities in GenAI
If you’re already experimenting with GenAI or looking to pivot your career, there are more opportunities than ever before to work on cutting-edge AI projects. Whether you’re seeking full-time roles, short-term contracts, or your first consulting project — the right opportunity is out there.
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