Building in Public. CS Graduate → AI/ML Engineer
Software Engineer (Building) •Python · Docker · Linux → AI/ML Engineer in 26 months
Msah Ambooka
CS graduate from Maseno University building toward an AI/ML Engineer role through a structured 26-month plan. Every week I ship a project to GitHub. Every phase ships a live deployment to ambooka.dev. The CS degree cuts Phase 1–2 study time by ~40%, letting me reach the AI/ML work faster. Currently deep in Phase 1: Python, Linux, Docker, SQL, and CI/CD.
The work I enjoy most sits at the intersection of useful products, dependable infrastructure, and clear communication. I like shipping things people can actually use, then tightening the system until it feels calm and intentional.
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Engineering Routine
Core Principles
Tech Stack & Tools
Building In Public
Proof of motion through public repos, system case-studies, and transparent roadmaps.
Off Hours
What I Build
Building an AI/ML Engineering roadmap across 5 phases — from Dockerised CLI to production multi-agent systems
Software Engineering
Python, TypeScript, Node.js REST APIs, React frontends, PostgreSQL, and Docker Compose — shipped to a live Hetzner VPS.
Cloud & Infrastructure
k3s Kubernetes, Helm, Terraform-managed AWS, Prometheus + Grafana observability — all GitOps-deployed.
Machine Learning
PyTorch models, HuggingFace fine-tuning, scikit-learn pipelines, FastAPI model serving with SHAP explanations.
AI / LLM Systems
RAG pipelines, LangChain LCEL, QLoRA fine-tuning, MLOps with drift detection, and multi-agent systems via LangGraph.
Let's build production-grade software and AI systems.
If you need practical engineering with strong ownership, I'm available for selective collaborations and full-time roles.
GitHub Activity
Clean snapshot of repository output, contribution rhythm, and active stack.
Contribution Heatmap
Language Mix
