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Hi, I'm Don Athalage

Welcome to my space on the internet. I'm a Cloud Platforms Engineer at ANZ Plus, with a background in full-stack software engineering, DevOps, and applied machine learning. This blog documents what I'm building, breaking, and learning along the way—both professionally and experimentally.

What I Do

I'm currently focused on:

  • Designing CI/CD patterns and infrastructure for cloud-native apps at scale.
  • Building with Terraform, GCP, and container platforms (Cloud Run, GKE, Istio).
  • Architecting cloud migration blueprints and internal developer platforms.
  • Exploring reinforcement learning, NLP, and real-time systems as side interests.

Work & Background

  • Cloud Platforms Engineer, ANZ Plus
    Leading internal platform engineering efforts with a focus on developer efficiency and scalable infrastructure.

  • Solutions Architect, Cloud Centre of Excellence
    Designed secure, performant cloud migrations across ANZ's portfolio.

  • Research Assistant, Deakin University
    Worked on deep learning pipelines for information extraction and NLP.

  • Education:
    Bachelor of Software Engineering (First Class Honours), Deakin University
    Academic research focused on combinatorial algorithms and applied ML.

Certifications

  • AWS Certified DevOps Engineer – Professional
  • AWS Certified Developer / Architect – Associate
  • GCP Associate Cloud Engineer

Projects & Experiments

  • 5G Network Resource Optimization with Knative
    Simulated virtual 5G networks and optimized function deployments using Knative autoscaling.

  • Reinforcement Learning for 5G Slicing
    Built learning models to dynamically manage network slice resources.

  • Exact Cover Algorithms with ZDDs
    Researched data structures and algorithms for solving NP-complete problems in C++ and Python.

You’ll find deeper technical write-ups for these in the blog soon.

Blog Topics

I write about:

  • Cloud infrastructure patterns and platform engineering
  • GCP, AWS, Terraform, Kubernetes, CI/CD pipelines
  • Dev productivity tooling and architecture
  • Experiments in machine learning, NLP, and systems
  • Lessons from past projects, failures, and side quests