Tech Odyssey: Navigating journey from Software Engineering to frontiers of AI Research

How did your journey in the tech industry commence?

I began my tech journey in 2019, as a Software Engineer at Xtrapay, a Fintech Company in Abuja. At that time, I had just finished my bachelor’s degree in software engineering from China. I decided to leave China after 5 years because I believed Nigeria would give me a better opportunity to develop my skills. At that time, people thought I was crazy for coming back to Nigeria. However, looking back, it was one of the best decisions I made because it enabled me to commence my career without the limitations I faced in China such as the language barrier and oversaturated labor market.

Transitioning from Software Engineering to AI Research is quite a leap. Can you walk us through the catalysts that led to this shift in your career path?

As I delved deeper into Software Engineering, the potential of AI to revolutionize technology fascinated me. The desire to be at the forefront of innovation and contribute to shaping the future propelled me toward AI Research. It’s an ever-evolving field with limitless possibilities.

Can you share a specific project or achievement in Software Engineering that you consider pivotal in your career journey?

One notable achievement would be leading the team as the CTO at Xtrapay. This experience not only honed my technical skills but also exposed me to the intricacies of managing large-scale systems, fostering a holistic understanding of software development.

What sparked your interest in Artificial Intelligence, and how did you decide to delve into research in this field?

My interest in AI was sparked by the transformative potential it holds. Witnessing AI’s impact on industries and its ability to solve complex problems fascinated me. The decision to delve into AI research, particularly in the Medical Sector stemmed from a desire to contribute to this rapidly evolving field and be at the forefront of technological innovation.

How do you manage to stay updated with the latest developments in both Software Engineering and Artificial Intelligence?

I prioritise continuous learning through online courses, conferences, and engagement with professional communities. Following reputable journals, blogs, and participating in industry forums helps me stay abreast of the latest advancements in both Software Engineering and AI.

Can you highlight a particularly challenging moment in your transition to AI Research and how you overcame it?

One of the challenges was adapting to the depth of theoretical knowledge required in AI research. Machine learning algorithms all stemmed from mathematically theoretical knowledge which has been present as far back as the 60s, so it is important to understand the theoretical knowledge in each algorithm so you can apply them accordingly. I overcame it by enrolling in specialised courses, seeking mentorship, and dedicating time to hands-on projects. Embracing the learning curve and persistently addressing gaps in my knowledge were crucial in overcoming this challenge.

As you navigated through different career phases, how did your skill set evolve, and what skills do you find most valuable in the current AI research landscape?

My skill set evolved from a strong foundation in programming to encompass advanced skills in machine learning, deep learning, and data science. Valuable skills in the current AI research landscape include proficiency in programming languages like Python, expertise in neural network architectures, and a deep understanding of statistical modeling and algorithms.

Are there specific ethical considerations or challenges you’ve encountered in the realm of AI research, and how do you address them?

Ethical considerations in AI are paramount. Ensuring fairness and avoiding biases in models is crucial. Regularly assessing the ethical implications of research, engaging in discussions within the research community, and staying informed about emerging ethical guidelines contribute to responsible AI development.

How do you balance the creative and logical aspects of your work, especially in the context of developing innovative AI models?

Balancing creativity and logic involves fostering a creative mindset while adhering to the rigors of logical reasoning. Experimentation and thinking outside conventional boundaries are crucial in developing innovative AI models. Regularly engaging with diverse perspectives and approaches also fuels creativity in problem-solving.

As technology continues to advance, what trends do you foresee shaping the future of AI research, and how can professionals prepare for these shifts?

The future of AI research will likely be shaped by increased collaboration across disciplines and the integration of AI into various sectors. Professionals should embrace continuous learning, stay updated on emerging technologies, and foster interdisciplinary skills to thrive in this dynamic landscape.

In your experience, how can the tech industry better support individuals navigating diverse career paths within the field, especially those making transitions like yours?

The industry can provide more structured mentorship programs, facilitate knowledge-sharing platforms, and encourage a culture of lifelong learning. Embracing diversity in career paths and acknowledging the value of varied experiences can contribute to a more vibrant and innovative tech community.

What advice do you have for aspiring tech professionals considering a similar odyssey from Software Engineering to AI Research?

Follow your passion, stay curious, and don’t be afraid to take on challenges. The tech industry is vast, and there are numerous opportunities for growth and exploration. Embrace every experience, and remember that the journey itself is as valuable as the destination.