Distributed Systems | Cloud & Infrastructure | Applied Machine Learning

I design and build large-scale systems across compute, networking, storage, and data platforms. My work sits at the intersection of distributed backend systems, infrastructure automation, and correctness at scale.

I am currently deepening my expertise through a Machine Learning Specialization, integrating intelligent modeling with production-grade data systems.

I am based at Sydney, Australia.

• Distributed backend architecture
• Infrastructure automation (compute + SDN)
• Systems reliability & concurrency
• Data integrity & large-scale migrations
• Linux-based environments across stack layers
• Applied machine learning foundations

Applied ML implementations covering regression, classification, feature engineering, encoding strategies, and evaluation methodologies. Focused on understanding bias-variance tradeoffs and production-aligned modeling workflows.

Core themes: statistical foundations, model evaluation, data-to-model pipelines.

Designed and implemented a core reconciliation engine ensuring data integrity and correctness during high-volume financial data processing and cloud migration initiatives.

Built a synthetic data generation system to simulate financial transaction patterns, edge cases, and stress scenarios.

A backend abstraction layer for dynamically constructing optimized queries based on runtime conditions. Designed for extensibility, composability, and performance awareness in data-intensive systems.

Core themes: abstraction design, runtime optimization, backend correctness.

Automation framework for software-defined networking workflows, enabling programmatic provisioning and configuration management.

Core themes: API-driven infrastructure, SDN orchestration, operational reliability.

Compute virtualization automation toolkit supporting lifecycle management and environment standardization.

Core themes: systems orchestration, infrastructure APIs, scalable automation patterns.

Systems-oriented backend experimentation emphasizing performance, modularity, and clean service boundaries.

Core themes: concurrency, service design, systems-level thinking.

Automation utilities for legacy virtual networking environments with emphasis on compatibility, integration, and operational continuity.

Core themes: real-world constraints, system integration, stability under change.

Languages: Python, Java Systems: Linux, distributed environments Cloud: AWS architecture patterns Infra Domains: Compute, storage, networking, SDN Backend: REST APIs, service design Data: Pandas, feature engineering, modeling fundamentals Tooling: Docker, Git

• Correctness before scale
• Observability by design
• Automate what repeats
• Build systems that degrade gracefully

Bridging distributed systems and machine learning — designing platforms where data reliability, system performance, and intelligent decision-making coexist.