About
About
Computer scientist working in DevOps and cloud. Backend depth in Python and FastAPI, with Go and Java where they fit. IEEE-published in NLP, and happiest somewhere near distributed systems design.
The long version
Biography
I came to infrastructure through computer science, not the other way round. I started with JavaFX at university like everyone else, went through MERN, and settled on FastAPI, SvelteKit and PostgreSQL because that combination let me build the things I wanted to build. Then I got interested in what happens to software after it is written — how it is deployed, how it scales, how it fails — and moved into DevOps and cloud on AWS.
What actually holds my attention is distributed systems design: how a system behaves under load, where it breaks, and why. I learn by taking things apart and putting them back together, usually breaking them on purpose first. Most of what I know about Linux, containers and networking came out of fixing something I had just broken.
Recent work: barqr, a QR and barcode microservice in Go, shipped as a signed distroless container with an SBOM, security scanning in CI and fuzz tests; and TaskFlow, a FastAPI service built on Clean Architecture with OWASP-mapped controls. Before those I co-authored an IEEE paper on fake news detection in Algerian dialect using fine-tuned Transformers and LLMs (RIF 2025), which is where my interest in serving models in production started.
I hold a Master's in Data Engineering and Web Technology from Université Ferhat Abbas Sétif 1, and I have just finished a year of national service in a computer science department. I am available now, and the direction I am heading is cloud and platform engineering.
Why I do this
Mission
To build and run distributed systems well — and to keep taking them apart until I understand why they behave the way they do.
What I believe
Values
- Clean and maintainable code
- Continuous learning and improvement
- Team collaboration and communication
- Automation and efficiency mindset
- Interest in scalable system design
- Open-source learning culture
Beyond the stack
How I work with people
Each of these names what backs it, because the claim on its own is worth nothing.
- Teaching & mentoring
- Spent a year of national service both building the IT department's systems and teaching colleagues to use them — programming fundamentals, full-stack web development, and the systems I had built.
- Technical writing
- ADRs, runbooks and API documentation in barqr and TaskFlow — in barqr the docs are asserted against the code in CI, so they cannot go stale.
- Self-directed learning
- About 70% through AWS Solutions Architect Associate on my own schedule, and shipped a complete service in Go, a language I had not used before starting it.
- Disciplined process
- Issue → branch → PR → squash-merge on every change, conventional commits, and CI that gates lint, security scanning and tests before anything merges.
- Systematic debugging
- I learn by breaking things on purpose and finding out why — most of what I know about Linux, containers and networking came out of fixing something I had just broken.
- Adaptability across stacks
- JavaFX at university, then MERN, then FastAPI, SvelteKit and PostgreSQL, and now Go and cloud infrastructure — each move driven by what the problem actually needed.
How I work
Working style
Preferred environment
remote-first, async-friendly, Linux-based development environment
Collaboration
engineering-focused teams, Git-based workflows with PR reviews, mentorship-oriented
Strengths
- Strong foundation in backend development and Linux systems
- Growing experience in DevOps tools and cloud concepts
- Understanding of distributed system principles
- Comfortable working across backend and infrastructure layers
Growth areas
- Gaining production experience with cloud-native systems and AI infrastructure
- Deepening AWS, Kubernetes, and Terraform skills in real-world environments
- Building industry experience through a first engineering role
- Strengthening system design and distributed architecture skills