who_am_i:
name: "Hitesh Prajapati"
role: "Cloud Engineer | DevOps WorkFlow"
education: "B.E. Information Technology @ LDRP Institute of Technology, Gandhinagar"
email: "hprajapati1606@gmail.com"
phone: "+91 8160532079"I am a results-driven Cloud Engineer and DevOps WorkFlows currently pursuing my Bachelor of Engineering in Information Technology at LDRP Institute of Technology and Research, Gandhinagar. My engineering philosophy centers on building production-grade systems that seamlessly bridge the gap between intelligent algorithms and scalable cloud infrastructure. With a strong foundation in full-stack development, machine learning operations, and cloud-native DevOps, I architect solutions that are not just functional but are built for resilience, observability, and zero-downtime deployment at scale.
My expertise spans the entire software delivery lifecycle — from writing clean, tested code in C++, Python, and JavaScript to containerizing applications with Docker, orchestrating CI/CD pipelines via GitHub Actions, and deploying to AWS cloud infrastructure. I thrive at the intersection of product engineering and platform engineering, where every commit is a deployable artifact, every pipeline is a quality gate, and every deployment is a confident rollout. My work on the Data-Agnostic MLOps Pipeline exemplifies this mindset — a modular, config-driven architecture that automates ML workflows from ingestion to deployment with Docker-containerized reproducibility.
I approach every problem with a product engineering mindset — understanding that great software is not just about code, but about delivering measurable impact through thoughtful system design, rigorous testing, and relentless iteration. Whether I am building a React + Node.js inventory management platform or an open-source DevOps utility hub with 12+ browser-native tools, I prioritize developer experience, operational excellence, and security by default.
Open To:
K8s Cluster GitOps Config — Enterprise CI/CD & GitOps Platform
An enterprise-grade, GitOps-driven Kubernetes CI/CD deployment platform that treats infrastructure as a living product. This repository serves as the single source of truth — ArgoCD continuously watches it and ensures the live AWS EKS cluster matches the declarative configurations stored in Git. Every commit is visualized, every deployment is tracked, every failure is self-healed, and every metric is observed in real-time through stunning dashboards. The platform implements a complete 8-stage CI pipeline (lint → test → SonarQube quality gate → Trivy security scan → Docker distroless build → push to ECR → update Kustomize manifests → ArgoCD sync) with progressive delivery across three environments.
Built with defense-in-depth security across 5 layers — edge (AWS WAF + ALB + rate limiting), transport (TLS 1.3 + Cert-Manager + mTLS), network (private subnets + NetworkPolicies), container (distroless + non-root + read-only filesystem), and application (Helmet.js + Sealed Secrets + OPA Gatekeeper). Infrastructure is provisioned via Terraform (VPC with 3 AZs, EKS cluster with KMS encryption, ECR, IAM roles with OIDC auth). Zero-downtime deployments guaranteed with rolling updates, readiness probes, and PodDisruptionBudgets.
| Aspect | Details |
|---|---|
| Stack | Kubernetes (EKS), ArgoCD, Terraform, GitHub Actions, Kustomize, Docker (Distroless), AWS (VPC, ECR, ALB, Route53) |
| Scale | 3 environments (Dev/Staging/Prod), HPA 3→20 replicas, 5,000+ req/s throughput, P99 latency < 95ms |
| Performance | Zero-downtime rolling updates, ~3s cold start, 85MB distroless images, auto-scaling on CPU 70% / Memory 80% |
| Security | 5-layer defense-in-depth, Trivy + SonarQube gates, Sealed Secrets, non-root containers, private subnets, KMS encryption, OIDC auth |
| Impact | Production-grade GitOps platform — single source of truth, self-healing, full observability (Prometheus + Grafana + Loki + Jaeger) |
| Repository | View on GitHub |
Data-Agnostic MLOps Pipeline
A modular, production-grade MLOps pipeline designed to automate the complete machine learning workflow — from data ingestion and preprocessing to model training and deployment. The architecture is config-driven, enabling structured and scalable ML workflow execution without code changes. It supports multiple data formats including CSV, Excel, and JSON out of the box. The entire pipeline is Docker-containerized to ensure reproducible, deployment-ready machine learning environments that can be versioned, tested, and deployed with confidence across any infrastructure.
| Aspect | Details |
|---|---|
| Stack | Python, Docker, MLflow, Config-Driven Architecture |
| Scale | Multi-format data pipelines (CSV, Excel, JSON) |
| Performance | Automated ingestion → preprocessing → training pipeline |
| Security | Docker containerization for isolated, reproducible environments |
| Impact | Zero-code-change pipeline reconfiguration; production-ready ML deployments |
| Repository | View on GitHub |
DevOps-Utility-Hub
An open-source, privacy-first platform containing 12+ tools purpose-built for DevOps engineers, system administrators, and developers. Built entirely with HTML, CSS, and JavaScript with zero backend dependency, ensuring full privacy and instant in-browser execution. Every tool runs locally in the browser — no data leaves the user's machine. This architecture eliminates server costs, removes privacy concerns, and delivers instant utility without installation or configuration, making it an indispensable toolkit for on-the-go DevOps workflows.
| Aspect | Details |
|---|---|
| Stack | HTML, CSS, JavaScript (Zero Backend) |
| Scale | 12+ tools for DevOps, sysadmin, and developer workflows |
| Performance | Instant in-browser execution, zero server dependency |
| Security | Full privacy — no data leaves the browser, no backend |
| Impact | Open-source community tool; zero-install DevOps toolkit |
| Repository | View on GitHub |
| Recognition | Details |
|---|---|
| Oracle Cloud Infrastructure Certified Architect Associate | Industry-recognized certification validating expertise in OCI architecture, compute, networking, storage, and identity management |
| Deloitte Australia Data Analytics Job Simulation | Completed professional data analytics simulation with Deloitte, demonstrating proficiency in business intelligence and data-driven decision making |
| AWS Cloud Practitioner Essentials | Foundational AWS cloud certification covering core AWS services, cloud concepts, security, architecture, and pricing models |
| Open Source Contributor — DevOps Utility Hub | Built and published 12+ open-source DevOps tools with zero-backend, privacy-first architecture for the developer community |
| MLOps Pipeline Architect | Designed a production-grade, config-driven MLOps pipeline supporting multi-format data processing with Docker containerization |
current_focus:
learning:
- "Advanced Kubernetes & Helm Chart Orchestration"
- "Production MLOps with MLflow & Kubeflow"
- "System Design at FAANG Scale"
- "Terraform Advanced Modules & State Management"
building:
- "Data-Agnostic MLOps Pipeline — Config-Driven ML Workflow Automation"
- "Enterprise DevOps Utility Hub — 12+ Tools, Zero Backend"
- "Cloud-Native Microservices Architecture on AWS EKS"
exploring:
- "Generative AI & LLM Fine-Tuning for Domain-Specific Applications"
- "eBPF-Based Observability for Kubernetes Workloads"
- "GitOps-Driven Infrastructure with ArgoCD & Flux"
- "Serverless Architecture Patterns with AWS Lambda & Step Functions"
open_to:
- "Full-Time Software Engineering Roles"
- "DevOps / Platform Engineering Positions"
- "AI/ML Engineering Opportunities"
- "Open Source Collaborations & Contributions"
- "Technical Consulting & Freelance Projects"
"Engineering is not about building things — it's about building things that scale, heal, and outlast the engineer."