Good software engineering is not a collection of techniques - it is a disposition toward systems.
Distributed Systems Engineer with over two decades of experience architecting information systems and administering declarative environments.
I build performant, scalable, and maintainable tools by leveraging the unique strengths of a deliberate core stack - high-concurrency services in Go, data and automation in Python, contracts enforced by Protocol Buffers. Each layer earns its place.
At the systems layer, the same principles apply: explicit over implicit, reproducible over fragile, observable over opaque. I design and operate infrastructure that treats configuration as code and repeatability as a hard requirement - NixOS for declarative host configuration, Terraform for cloud provisioning, Ansible for orchestration, QUIC and SDN at the network boundary. The goal in each case is a system whose state is fully described, reproducible from scratch, and auditable.
My operating thesis: systems engineering and software engineering are not separate disciplines but two expressions of the same underlying concern. Build a system without writing software and you are assembling someone else's decisions. Write software without understanding the system it runs on and you are optimizing in the dark. In practice, serious work in either domain demands competence in both - the ratio shifts by context, but the dependency is constant.
That dual fluency shapes how I approach problems: from the machine upward and from the architecture downward, until the two meet cleanly.
A Distributed Systems Engineer with a focus on systems programming and declarative infrastructure. Adept in applying the scientific method to system design - utilizing hypothesis-driven development and empirical observation to ensure objective, high-reliability results. Dedicated to continuous improvement and creative technical orchestration.
From at to zcat. From DNS to email. I possess the skills to configure and administer your systems.
Whether it is consensus or discovery I can navigate a route through the hive.
Protobufs maintain strictly typed, language-agnostic, and lightning-fast communication contracts.
From build tooling to design patterns I can craft anything given the resources.
A unique, declarative Linux distribution built on top of the Nix package manager, where the entire operating system configuration - including packages, system services, and configuration files - is managed via a single functional language.
Software-Defined Networking (SDN), Cloud and Data Center Networking, Websockets, and QUIC are of prime interest.
Powering high-concurrency backend services, CLI tools, and systems that require raw performance and fast execution.
Whether it is NixOS or Terraform and Ansible I have the experience to overcome your deployment gaps.
The province of data processing, scripting, automation, and rapid prototyping within the ecosystem.
Applying declarative system architectures to constrain, orchestrate, and bring reproducibility to probabilistic AI models.
Go and Python with Protocol Buffers is a proven stack for high-performance, cross-language microservices. Go owns performance-critical services; Python handles data science, ML, and automation. Communication is anchored by centralized .proto files defining shared data structures and service contracts, from which the toolchain automatically produces the code each language needs to send and receive structured messages.
The breadth above is not accidental. Sustained curiosity - the habit of pulling threads past the point of immediate utility - is what connects distributed systems to NixOS to AI agent orchestration. Exploration and experimentation are the mechanism; the knowledge is the residue.
The best systems are the ones nobody notices - until they're gone.
Good software engineering is not a collection of techniques - it is a disposition toward systems. It is the habit of asking what a thing actually does before asking how to make it faster, the reflex to name failure modes before celebrating success paths, and the discipline to leave a codebase more legible than you found it. These are not taught in a curriculum. They are earned through years of maintaining systems that outlive their original authors, debugging production failures at 2 AM, and learning - repeatedly - that complexity is not a sign of sophistication but of deferred decisions.
I have spent more than two decades cultivating that disposition. My career traces the full vertical of modern infrastructure: from the machine-level concerns of configuration management and network provisioning, through the architectural challenges of distributed systems and high-concurrency services, to the emerging frontier of agentic AI. What connects those layers is not the technology but the underlying commitment to systems that are correct, observable, and honest about their failure modes.
Information technologies are equally prominent throughout my personal endeavors. Whether it is the daisy-chained topographies of MIDI (Musical Instrument Digital Interface) networks. Or gain staging - the process of managing audio signal levels at each stage of a recording or mixing chain to ensure optimal sound quality, maximize headroom, and minimize noise and distortion. Music production and engineering, both theory and practice, are just systems for me to study, engage, and re-engineer.
The same instincts that drive my engineering practice show up in how I make music. I prefer hardware synthesis over software production not out of nostalgia but for the same reason I prefer explicit systems: tactile hardware forces you to commit. A patch cable is a contract. A knob position is a decision. There is no undo history on a modular synthesizer, and that type of constraint produces a different kind of attention. The machine pushes back, and the push-back is the point.
If you have read this far, thank you. The time you spent here is not something I take lightly. Whether you are evaluating a hire, exploring a collaboration, or simply curious - I hope the document gave you an honest picture.