AI / Machine Learning Engineer

An AI/ML Engineer in Kenya starts by training deep learning models for computer vision applications in agriculture or healthcare. They preprocess data from local IoT sensors, optimize model inference for edge devices, and collaborate with domain experts to validate outputs. Afternoons involve deploying models via APIs and monitoring performance in production.

A bachelor's in computer science, mathematics, or statistics from universities like Strathmore or JKUAT is common, with many employers preferring a master's in machine learning from institutions like AIMS or the African Institute of Science and Technology. Entry-level roles start as data analysts or research assistants at labs like the IBM Research Lab in Nairobi or iHub. Practical experience is built through Kaggle competitions, open-source contributions, and online courses (e.g., Coursera's Machine Learning by Stanford). Many entry hires join fintech startups like Branch or Tala as junior ML engineers, focusing on credit scoring models.

Career milestones progress from junior ML engineer (1-3 years, ~1.5M KES) to ML engineer (3-5 years, ~3M KES), then senior ML engineer (5-8 years, ~5M KES), and eventually head of AI or ML architect (8+ years, 7M+ KES). After 10 years, you might lead AI strategy at a bank or co-found a healthtech startup, like mPharma. Salary growth is 20-30% per promotion, with premium pay for NLP and computer vision specialization. The trajectory often includes moving from pure modeling to MLOps and responsible AI governance.

Kenya's AI/ML market is booming, especially in fintech (M-Pesa fraud detection, KCB loan scoring), agriculture (precision farming via startups like Apollo Agriculture), and healthcare (AI diagnostics at mPharma). Leading employers include Safaricom, Equity, Cellulant, and global tech firms like Google and Microsoft with AI research hubs in Nairobi. The market is concentrated in Nairobi's innovation corridor, but remote roles are rising. Growth drivers include government's digital economy push, lower cloud costs, and a growing pool of data scientists trained at local bootcamps.

My day begins at 9am with a data pipeline check—ensuring daily transaction feeds from M-Pesa are clean and complete. By 10:30, I'm in a scrum meeting discussing feature engineering for a new credit risk model. I spend the afternoon training a transformer model on a GPU instance in AWS SageMaker, tuning hyperparameters and reviewing loss curves. At 3pm, I present preliminary results to product managers, explaining trade-offs between accuracy and inference time. The day ends with writing documentation and pushing code to a Git repository, often catching up on AI papers during the commute home.

AI/ML engineers are in demand in Kenyan fintech, healthtech, and agriculture tech startups. Data science bootcamps like Moringa and DataCamp are popular entry points. However, the market is still nascent with fewer senior roles.