I'm Malusi Thandolwethu Nathan Nkosi, a Software Engineering student at Eduvos in Durban. I completed my Higher Certificate in Information Systems in 2024 and am now in my second year of a BSc in Software Engineering, with a deepening focus on cloud engineering, DevOps, and AI/Machine Learning.
I'm drawn to the intersection of security, automation, and scalable infrastructure. The best software is secure by design, automated where possible, and built to scale — that's the philosophy I bring to every project: security-first thinking, clean CI/CD workflows, and cloud infrastructure built with purpose.
My foundation spans full-stack development in C#, Java, JavaScript, and .NET — applied across web, mobile (Flutter), and AI-integrated projects. I have practical exposure to AWS, Docker, and Linux environments, and I'm always working to understand the full DevOps lifecycle from commit to deployment.
Growing up bilingual in Durban has shaped how I communicate — clearly, contextually, and with empathy. I bring that same quality to technical documentation, team collaboration, and code review. I thrive in teams that value continuous learning and aren't afraid to question why things are done a certain way.
Currently Learning
AWS Cloud Practitioner
IAM, EC2, S3, VPC, shared responsibility model, and cloud pricing fundamentals.
Azure Fundamentals (AZ-900)
Multi-cloud exposure across Microsoft Azure services, deployment, and security models.
Docker & Kubernetes
Containerisation patterns, multi-stage builds, and container orchestration at scale.
DevSecOps Practices
Integrating SAST, automated security testing, and vulnerability scanning into CI/CD pipelines.
Work History
Independent Developer & Engineering Student
Portfolio Projects — DurbanBuilding portfolio projects across DevSecOps, cloud infrastructure, and AI tooling. Projects include a CI/CD-integrated secure task manager, an NLP resume analyser, and a full-stack banking system on AWS.
Data Capturer
Aurora Medical Practice — DurbanManaged and captured patient records under strict confidentiality. Assisted in partially automating data capture workflows to reduce manual entry and improve record accuracy.