I build full-stack features that make crypto portfolio tools easier to use and maintain. That includes leading adoption of AI-driven features, improving system reliability under load, and overall leaving the codebase better than I found it.

📌𝐑𝐨𝐥𝐞

Sylvanus is a startup helping users analyze and manage risk in digital asset portfolios. On a small, high-trust team, I work with quantitative developers and data engineers on ETL pipelines, data visualization, and backend services that turn analytics into actionable insights. My focus leans backend heavy, using Go, React, and Python, while also shaping infrastructure and guiding the technical side of AI feature integration. I follow SOLID principles to write code that is easy to understand, test, and maintain.

🚀 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬

Originally hired on contract to address Lambda ETL jobs hitting the 15-minute limit, I migrated them to ECS Fargate to improve reliability and throughput. After consistently delivering across the stack, the team created a full-time position for me, which I was grateful to accept. I resolved recurring memory issues in our Go backend that caused crashes under large data loads. More recently, I’ve led adoption of AI-powered features into the product using OpenAI, MongoDB vector indexes, and the Pydantic AI agent framework. Whether improving developer experience or stabilizing infrastructure, I aim to build tools that are solid, scalable, and easy to maintain.

🛠️ 𝐒𝐭𝐚𝐜𝐤

🤝 𝐇𝐨𝐰 𝐖𝐞 𝐖𝐨𝐫𝐤

We stay lightweight on process and heavy on ownership. I focus on shipping responsibly, improving systems, and being easy to work with.

I jumped into ZenML’s VS Code extension as a community contributor and ended up shipping two major features to a tool with over 1,800 downloads.

📌 𝐑𝐨𝐥𝐞 & 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬

I worked on two major features for ZenML’s VS Code extension: a DAG visualizer to help users see their pipeline structure at a glance, and a deployment panel for configuring and managing MLStacks. I started the DAG visualizer by pair coding with another contributor, then finished both features independently. While building them, I ran into and fixed multiple issues, including a bug with Watchdog file change detection and compatibility problems with newer ZenML client versions. I extended the TypeScript frontend, integrated it with the existing Python backend via the Language Server Protocol, and focused on making the experience intuitive for everyday users. The work was unpaid and freelance, but I approached it with the same level of care I bring to production roles.

⸻

🛠️ 𝐒𝐭𝐚𝐜𝐤

🤝 𝐇𝐨𝐰 𝐖𝐞 𝐖𝐨𝐫𝐤𝐞𝐝

I started off pairing with another contributor on the DAG visualizer, then took full ownership of both features. I worked independently but checked in regularly with a ZenML engineer. Each feature was scoped, built, and documented with long-term maintainability in mind, and I treated every issue like production work.

I helped build a TypeScript-powered workflow engine focused on fast iteration, clean developer tooling, and asynchronous job orchestration at scale.

📌 𝐑𝐨𝐥𝐞

Reverb was a small, four-person engineering effort focused on creating a robust workflow engine that prioritized developer experience. The idea was to let engineers define workflows using TypeScript and run them as distributed, event-driven systems without managing orchestration themselves. I worked on backend infrastructure, queue orchestration, API schemas, and deployment tooling. Most of my time went into writing clean, testable services and shaping a platform that felt simple to use but powerful under the hood. We moved quickly and shipped a lot, but the project ultimately didn’t reach production or adoption.

🚀 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬

I implemented job orchestration logic using Graphile Worker and PostgreSQL, built out API layers with Express, and defined how workflows were represented and triggered. I added developer tooling for debugging and logging, containerized services for ECS, and maintained CI flows with GitHub Actions. I also ran load testing with Artillery.io and handled end-to-end test flows in Postman. Communication between our queue workers and custom workflows was powered by JSON-RPC, which I helped integrate and support. Throughout, I focused on maintainability, clarity, and fast feedback loops.

🛠️ 𝐒𝐭𝐚𝐜𝐤

🤝 𝐇𝐨𝐰 𝐖𝐞 𝐖𝐨𝐫𝐤𝐞𝐝

We worked in a fast-paced, collaborative environment with daily standups, pair programming, and async communication. I shared ownership of system design and delivery, focusing on clean interfaces and testable systems others could build on.

I built tools, tracked down flaky systems, and wrote the docs we needed. It wasn’t called engineering, but it felt like it: find the problem, script the fix, and leave it better than before.

📌 𝐑𝐨𝐥𝐞

At Stefanini, I supported a high-volume Nike logistics hub by diagnosing and resolving hardware, software, and network issues across the warehouse and office. Most of my time went into solving recurring problems, documenting effective fixes, and figuring out why issues happened in the first place. That root-cause-first mindset (breaking down problems, isolating variables, and testing until the issue was clear) now serves me in software just as well.

🚀 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬

I streamlined support for a persistent duplicate print issue by writing a C# program that scanned DMP spool files for repeated jobs. That surfaced a timeout-triggered failover to a second print server, which we were then able to resolve. I also wrote a C# tool to poll Zebra printers for label usage data, giving the team better visibility into supply levels. When recurring issues lacked documentation, I created internal guides for common problems with monitors, docks, and label printers so tickets could be closed faster and with more confidence.

💻 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲

🤝 𝐇𝐨𝐰 𝐖𝐞 𝐖𝐨𝐫𝐤𝐞𝐝

I collaborated with warehouse control systems, networking, and operations teams. We met daily for standups and used ServiceNow to manage tickets and escalate site-wide issues.