Machine Learning Engineering Internships in Kenya
Machine Learning Engineering internships provide hands-on experience in designing and implementing scalable machine learning systems, building ML pipelines, and deploying AI models in production environments in Kenya's growing AI infrastructure landscape. Typical duration: 3–12 months.
What You'll Do
Design ML Pipelines
Build end-to-end machine learning pipelines for data processing, model training, and deployment.
Deploy ML Models
Deploy machine learning models into production environments and ensure scalability and reliability.
Implement A/B Testing
Design and implement A/B testing frameworks for model evaluation and performance optimization.
Optimize Feature Engineering
Develop and optimize feature engineering processes to improve model performance and accuracy.
Monitor Model Performance
Implement monitoring systems to track model performance and detect drift in production.
Build MLOps Infrastructure
Create MLOps infrastructure for automated model training, testing, and deployment workflows.
Skills You'll Gain
Who Should Apply
Year of Study
3rd and 4th year Computer Science, AI, or related students with strong programming and system design skills.
Prerequisites
Strong programming skills (Python), understanding of machine learning, and basic knowledge of system architecture.
Ideal Candidates
Students with strong problem-solving skills, interest in system design, and passion for building scalable AI solutions.
Academic Requirements
Minimum GPA of 3.2 and completion of machine learning, software engineering, and system design courses.
Typical Host Companies
Duration & Mode
Duration
3–12 months (flexible based on company needs and student availability)
Schedule
Full-time during breaks, part-time during semester (20-40 hours/week)
Mode
Hybrid (mix of on-site and remote work)
Supervision
Direct mentorship from senior ML engineers and regular check-ins with university coordinator
Related Career Pathways
Next Steps: Advanced Internships
Future: Graduate Roles
Frequently Asked Questions
What types of ML engineering projects will I work on during the internship?
You'll work on various ML engineering projects including building ML pipelines, deploying models to production, implementing A/B testing frameworks, and creating MLOps infrastructure. The specific projects depend on the company's AI applications and infrastructure needs.
Do I need to know cloud platforms for ML engineering?
Basic knowledge of cloud platforms (AWS, Azure, GCP) is helpful but not required initially. You'll learn cloud-based ML services and deployment during the internship. Focus on understanding machine learning, programming, and system design concepts first.
What programming languages and tools will I use?
Common tools include Python (scikit-learn, pandas, numpy), ML frameworks (TensorFlow, PyTorch), cloud platforms, containerization (Docker), and orchestration tools. The specific tools depend on the company's technology stack and ML infrastructure.
Will I work on production ML systems during the internship?
Initially, you'll work in development and testing environments. As you gain experience and demonstrate competence, you may work on production ML systems under close supervision. Safety, reliability, and learning are prioritized throughout the process.
Ready to Start Your ML Engineering Journey?
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