Muath Alsawaier
Software Engineering student at Washington State University and SWE intern at Schweitzer Engineering Laboratories, where I build Python libraries and extensions for RTAC and write controller logic in Structured Text. Every demo on this page runs the actual code from its repository.
[ who i am & what i do ]
I'm a Software Engineering student at Washington State University and a software engineering intern at Schweitzer Engineering Laboratories, where I build Python libraries and extensions for the RTAC platform.
My focus is backend systems and developer tooling: APIs whose contracts are tested before they ship, static analysis engines that find flaws before production does, and performance data you can watch while it happens. I care about the unglamorous parts of software: the tests, the gates, the measurements. That's what separates code that works from code that's proven to work.
Building Python libraries and extensions for the RTAC platform that ship to production and are used by many SEL clients, including reusable utilities, schema-aware upgrades with tests, and a context manager that made library load and save up to 15x faster.
Took technical ownership of validating two regulated laboratory systems backed by SQL Server, in environments with minimal existing documentation: T-SQL checks on data integrity and audit trails, plus automated OQ/UAT test suites in pytest, pyodbc, and pandas built to 21 CFR Part 11 and GDPR.
[ technical proficiency ]
[ built, tested & running live ]
These are my own projects, built end to end, and so is this site, which runs on the same Python backend that powers its live demos. Every demo below runs the actual code from the repository behind it.
An API's spec is the source of truth. OpenAPI contracts are defined before implementation, and every change is scored against them: breaking changes fail loudly instead of surfacing in production.
pytest suites run on every commit behind GitHub Actions gates, with ruff and mypy in the loop. The tests encode the contract from step one, so a green build is a statement about behavior, not just about code that runs.
The profiler attaches to real workloads and streams CPU, wall-clock, and memory per call. Flame graphs show where time actually goes first, so optimization targets evidence instead of guesses.
Every release passes the vulnerability scanner for injection flaws and hardcoded secrets. Findings are tracked over time, so security regressions get caught in CI instead of by users.
Self-hosted platform that ingests OpenAPI specs and maintains living contracts: visual diffs, breaking change detection, and commit-level spec history.
Production-grade Python instrumentation with live metric collection, real-time flame graphs, and hot path detection.
Static analysis engine that detects SQLi, XSS, and leaked secrets, with prioritized, fix-tracked reporting.
Retrieval-augmented code assistant that answers questions about a codebase with grounded, cited answers: per-symbol chunking, cosine retrieval, and local LLM synthesis, plus a zero-model offline mode so the demo runs anywhere.
[ get in touch ]
A question about one of these projects, an idea worth pressure-testing, or just to trade notes on backend tooling: my inbox is always open!