package main
import "fmt"
type Engineer struct {
Name string
Role string
Location string
Stack []string
LeetCode string
OpenTo string
}
func main() {
me := Engineer{
Name: "Om Anand Dubey",
Role: "Backend Engineer | Ex-TCS",
Location: "India 🇮🇳",
Stack: []string{"Go", "Java", "Spring Boot", "Kafka", "Kubernetes", "Docker", "PostgreSQL", "Redis"},
LeetCode: "Top 0.1% Globally — 1500+ Problems",
OpenTo: "SDE / AI Engineer / Gen AI Engineer",
}
fmt.Println("Building systems that hold up under load — and stay observable while they do it.")
_ = me
}| Platform | Achievement |
|---|---|
| 🟡 LeetCode | Top 0.1% Globally — 1500+ Problems Solved |
| 🟢 GeeksforGeeks | Top Campus Performer — 1000+ Problems Solved |
| 📊 Total | 2500+ DSA Problems across all platforms |
Languages
Backend & Frameworks
Data & ML
Infrastructure & DevOps
Databases
AI / LLM / Agentic
Production-style fintech backend spanning six Spring Boot microservices that talk to each other over gRPC, coordinated through a transactional outbox → Kafka pipeline for at-least-once delivery with zero dual-write bugs. Ships idempotent transfers, optimistic-locking concurrency control with jittered retries, Redis-backed rate limiting, and OAuth2/OIDC auth via Keycloak with JWT re-validated at every hop fully observable with Prometheus + Grafana and deployable to Kubernetes with HPA and health probes.
Java 21 Spring Boot 3 gRPC Kafka PostgreSQL Redis Keycloak Kubernetes
AI-assisted incident investigation and remediation platform. A LangGraph-orchestrated multi-agent workflow (log, service health, deployment, and RAG agents) aggregates evidence and proposes root-cause diagnoses, gated behind a human-approval step before any sandboxed remediation runs. Backed by a real pgvector RAG pipeline and an MCP tool server exposing safe, read-only agent tools — fully containerized with Docker Compose.
Python FastAPI LangGraph PostgreSQL pgvector Redis Docker MCP
AI decision intelligence platform that turns natural-language business questions into SQL analytics, ML churn predictions, and demand forecasts. Built with FastAPI and two trained XGBoost models (0.95 AUC churn classifier, walk-forward demand forecaster), a Streamlit dashboard, and a rule-based recommendation engine — deployed via Docker on Render with a Neon Postgres backend.
Python FastAPI XGBoost PostgreSQL Streamlit Docker
Asynchronous notification system built with Spring Boot and Apache Kafka, with idempotent producers, DLQ-backed retry handling, and a full Prometheus + Grafana observability stack — containerized and Kubernetes-ready.
Java Spring Boot Kafka Docker Kubernetes Prometheus Grafana
Distributed URL shortener in Go with Redis caching, PostgreSQL persistence, multi-replica Kubernetes deployment, and full CI/CD via GitHub Actions. Production-grade observability built in with Prometheus and Grafana.
Go PostgreSQL Redis Docker Kubernetes Prometheus GitHub Actions
Enterprise-grade RAG platform for natural language querying across organizational documents — built with FastAPI, LangChain, and pgvector for semantic retrieval, powered by GPT-4o-mini with JWT-secured multi-user access.
Python FastAPI LangChain OpenAI PostgreSQL pgvector
🛒 Green Cart • Live ↗
Full-stack grocery delivery platform containerized with Docker, with Stripe payment integration, deployed to production.
Node.js React MongoDB Docker Stripe
🤖 Quick AI SaaS • Live ↗
Full-stack PERN app integrating Gemini API, ClipDrop, Cloudinary, and Clerk Auth for AI content generation.
PostgreSQL Express React Node.js Gemini API
Smart chatbot powered by Groq API and LLaMA 3.1 with session persistence and real-time replies.
Python Groq API LLaMA 3.1
Assistant System Engineer Trainee — Tata Consultancy Services (March 2026 – June 2026)
Built a Spring Boot backend with MySQL and JWT-based RBAC across 3 user roles, along with 5+ RESTful APIs for auth and booking management, backed by JUnit 5 and Mockito test coverage.
AI/ML Intern — IBM SkillsBuild × AICTE (May–July 2024)
Built a Mental Health Fitness Tracker analyzing data across 150+ countries using 6+ regression models. Achieved 98.5% accuracy with Python and Scikit-Learn.
Lead, AI/ML Team — Google Developer Student Club (GDSC-GGV)
Mentored peers on AI/ML projects, model deployment, and hackathon preparation.
🎓 B.Tech — Computer Science & Engineering | Guru Ghasidas Vishwavidyalaya
✅ Oracle Cloud Infrastructure 2025 — Generative AI Professional ✅ Oracle Cloud Infrastructure 2025 — AI Foundations Associate