Jehpte Ioudom Shares Data and AI Career Path from Cameroon

In Short

• Jehpte Ioudom, a Cameroonian engineer, shared his career story in Techpoint’s After Hours series.
• He describes how an internship at Ericsson Cameroon shaped his interest in data and practical digital tools.
• He now works across data engineering, cloud systems, and AI consulting through his practice, Foubslabs.

What Happened

Jehpte Ioudom, a Cameroon-based AI engineer, is the latest guest in Techpoint’s After Hours interview series. In the feature, Ioudom explains how early access to a home computer in Douala turned into a career after a formative internship at Ericsson Cameroon.

During that internship, Ioudom was assigned to help research problems in corporate travel management. Staff were manually reporting receipts and expenses, and management wanted clearer insight into what employees were experiencing.

A fellow intern introduced him to Google Forms and Google Sheets. These are basic data collection tools, think of them as online questionnaires and a spreadsheet that fills itself automatically. Ioudom says building a survey for travelling engineers helped him see how software could collect information at scale and support business decisions.

From that early exposure to data collection and analysis, he went on to work on data engineering, cloud systems, and AI projects across Europe and Africa. Today, he runs Foubslabs, positioned as an AI consulting practice.

Why It Matters

Career stories like Ioudom’s are a useful window into how African technical talent is being shaped, often through practical workplace problems rather than formal AI research tracks.

It also highlights a common pattern in enterprise and telecom environments. Small internal workflow fixes, like moving from paper expense reports to digital forms, often become the entry point into analytics and automation work.

For founders and operators hiring data and AI talent, the takeaway is straightforward. Many strong practitioners build their skills by solving operational problems first, then moving into larger data infrastructure, cloud platforms, and AI systems over time.