Full Stack · Cloud · DevOps · AI
Balraj Yadav — production-focused engineer. Browse the highlights below, or grab the PDF.
Profile
Balraj Yadav
Also known as Balu Yadav on GitHub.
Production-focused full-stack developer building cloud, DevOps, automation, observability, and AI-assisted engineering workflows. I ship product features in Next.js, package and deploy them with Docker and GitHub Actions, and monitor what they do in production.
Contact
- Email · balraj.codes@gmail.com
- Web · www.balraj.dev
- GitHub · github.com/SkyCodeLab
- Location · Remote · India
Featured projects
Selected work
Production-style monitoring lab — Docker Compose, Prometheus, Grafana, Loki, Nginx + TLS, Terraform, runbooks.
GitHub Actions workflow with typecheck, lint, tests, multi-stage Docker build, and a gated deploy job.
Architecture study for an LLM-assisted incident triage tool with citations and tool-use.
Architecture study for a dashboard that ranks AWS waste in projected monthly savings.
Tools
What I work with
Frontend
Next.js (App Router) · React 19 · TypeScript · Tailwind CSS
Backend
Node.js · Express · REST APIs · PostgreSQL
Cloud & DevOps
AWS (EC2 / S3 / IAM) · Docker & Compose · Linux administration · Nginx + TLS · GitHub Actions · Terraform (IaC)
Observability
Prometheus · Grafana · Loki + Promtail · Alertmanager · Runbooks · SLOs & dashboards
AI Engineering
LLM workflows · RAG patterns · Prompt engineering · AI-assisted debugging · DevOps copilots · Claude API & Code
Engineering Practices
CI/CD pipelines · System design basics · Documentation · Runbooks & incident notes · Deployment thinking · Code review with AI
Designations & journey
Roles, learning phases, and what I shipped along the way.
Full Stack Developer
Hitech People Inc.
Jan 2024 — Present
Building web applications and internal tooling while growing into cloud and DevOps. Pairing day-to-day product work with deliberate practice in CI/CD, containers, and AI-assisted engineering.
- Contributing to Next.js applications used internally and by clients
- Practicing Docker, GitHub Actions, and Linux administration on real workloads
- Using AI tools (Claude, Cursor) to accelerate review, refactors, and documentation
Next.jsTypeScriptNode.jsDockerGitHub ActionsSelf-directed cloud & DevOps track
Personal study
Jun 2023 — Present
Structured learning path through Linux, networking, Docker, CI/CD, and cloud — alongside the day job. Output is captured in the Cloud & DevOps Lab section and the writing notes on this site.
- Linux administration: SSH hardening, systemd services, shell scripting
- Containers and CI/CD: multi-stage Docker builds, GitHub Actions pipelines
- Cloud foundations: AWS EC2/S3/IAM hands-on, Terraform reading and writing
- Observability lab: Prometheus, Grafana, and Loki on a single VPS
LinuxBashDockerGitHub ActionsAWSTerraform
What I work with
Grouped by area, with proof of work where it exists. No meaningless progress bars — just where the skill has been used.
Component-driven UIs with strong typing and accessibility.
- Next.js (App Router)This portfolio
- React 19
- TypeScript
- Tailwind CSS
API design, server-side logic, and persistence.
- Node.js
- Express
- REST APIs
- PostgreSQL
Repeatable provisioning, packaging, and delivery.
- AWS (EC2 / S3 / IAM)
- Docker & Compose
- Linux administration
- Nginx + TLS
- GitHub Actions
- Terraform (IaC)
Metrics, logs, and alerts that surface real failure modes.
- PrometheusInfra Monitor lab
- Grafana
- Loki + Promtail
- Alertmanager
- Runbooks
- SLOs & dashboards
Practical LLM workflows that pay back in engineering time.
- LLM workflows
- RAG patterns
- Prompt engineering
- AI-assisted debugging
- DevOps copilots
- Claude API & Code
How the work gets shipped, kept honest, and learned from.
- CI/CD pipelines
- System design basics
- Documentation
- Runbooks & incident notes
- Deployment thinking
- Code review with AI