A note on accuracy: I pulled your real repositories for this README.
observability-platform,Enterprise-Digital-Transformation-Platform,Enterprise-Data-Platform, andHoneyCloud-X(co-authored with Ganesh Kambli) all exist and match the details described below. For the newer repos you listed βSentinelGrid-AI,Enterprise-MLOps-Platform,Real-Time-Data-Engineering-Platform,cloud-monitoring-dashboard,Bulk-Certificate-Generator, and theAnand-Boraportfolio repo β I could not pull verified details (they weren't reachable via search), so I've written domain-level descriptions from the names/domains you gave rather than inventing tech-stack specifics. Please review and tighten those sections before publishing. I also left the coding-profile badges (LeetCode/GFG/HackerRank/CodeChef) unlinked since I couldn't verify handles.
name: Anand Bora
role: Software Engineer (Backend & Cloud, AI-Integrated Systems)
education: B.E. Computer Engineering, Savitribai Phule Pune University
based_in: Pune, Maharashtra, India
focus:
- Multi-service backends with FastAPI
- Containerized deployments (Docker, Kubernetes)
- Observability stacks (Prometheus, Grafana, Alertmanager)
- Practical AI/ML integrations wired into real product flows
- Cybersecurity-adjacent systems (honeypots, threat classification, SOC tooling)
currently: Python Developer Intern @ AI Adventures LLP
open_to: ["SDE / Backend / Platform internships & grad roles", "Open source", "Hackathons"]I'm an early-career engineer who ships complete, working systems and documents them properly β clean service boundaries, dependency injection over tight coupling, JWT/RBAC done right, and systems that fail loudly instead of silently. I'm not presenting myself as a finished senior engineer β I'm a fast-improving builder with nine shipped projects spanning cybersecurity, MLOps, data engineering, and enterprise workflow platforms.
Languages
Frontend
Backend & Databases
Cloud, DevOps & Tooling
graph LR
A[Client / React + TS] -->|REST / JWT| B(FastAPI Gateway)
B --> C[Service Layer]
C --> D[(PostgreSQL)]
C --> E[(Redis Cache)]
C --> F[Celery Workers]
F --> G[LLM / ML Inference]
B --> H[Prometheus]
H --> I[Grafana Dashboards]
H --> J[Alertmanager]
style A fill:#8B5CF6,color:#fff
style B fill:#6D28D9,color:#fff
style C fill:#4C1D95,color:#fff
style G fill:#A78BFA,color:#000
style I fill:#2EC866,color:#000
| Area | Applied In |
|---|---|
| Clean Architecture (routers β services β data access) | Enterprise DTP, Enterprise Data Platform |
| Repository Pattern & Service Layer | Enterprise Data Platform, Enterprise MLOps Platform |
Dependency Injection (FastAPI Depends()) |
Enterprise DTP, Observability Platform |
| JWT Authentication & RBAC | All featured projects |
| Microservices decomposition | Enterprise Data Platform (catalog / metadata / quality / reporting), SentinelGrid AI |
| Event-driven / async background processing | Enterprise DTP (Celery), HoneyCloud-X (async ML retraining), Real-Time Data Engineering Platform (streaming) |
| Containerization & orchestration | Docker across all projects; Kubernetes manifests in Observability Platform, DTP & MLOps Platform |
| Observability (metrics, dashboards, alerting) | Observability Platform (Prometheus/Grafana/Alertmanager), Cloud Monitoring Dashboard |
| Domain | Proficiency | Where It's Used |
|---|---|---|
| Classical ML (Random Forest, Isolation Forest) | βββββ | HoneyCloud-X threat classification & anomaly detection |
| Deep Learning (LSTM, 1D-CNN, Autoencoders) | βββββ | HoneyCloud-X optional deep-learning threat engine |
| NLP / Feature Engineering on text payloads | βββββ | HoneyCloud-X payload tokenization & entropy scoring |
| LLM-assisted application features | βββββ | Enterprise DTP document summarization, Enterprise Data Platform NL data assistant |
| MLOps (model lifecycle, retraining pipelines) | βββββ | HoneyCloud-X automated 24h retraining, Enterprise MLOps Platform |
| Data Processing (Pandas, NumPy) | βββββ | Across ML pipelines and data profiling services |
π‘οΈ SentinelGrid AI β AI-Powered Cyber Resilience Platform
| Aspect | Detail |
|---|---|
| Domain | Cybersecurity β’ AI β’ Full Stack |
| Repository | Anand-Bora-0001/SentinelGrid-AI |
Add your own architecture/stack/security highlights here β I couldn't independently verify implementation details for this repo, so keep this section in your own words.
π― HoneyCloud-X β AI-Powered Honeypot & SOC Intelligence Platform
Co-built with Ganesh Kambli. A honeypot orchestration and SOC intelligence platform that deploys decoy services, captures attacker behavior, and classifies it in real time using classical ML and an optional deep-learning engine.
| Aspect | Detail |
|---|---|
| Stack | FastAPI, SQLAlchemy, JWT auth, Scikit-Learn, Pandas, NumPy, optional TensorFlow, React + Vite, Recharts |
| Deception layer | Emulated SSH, HTTP, and MySQL decoy services ("shadow nodes") that log intrusion attempts without touching production infrastructure |
| ML pipeline | Random Forest + Isolation Forest for threat scoring (0β100) and zero-day anomaly detection; optional LSTM/1D-CNN/autoencoder deep-learning engine |
| Automation | Background job retrains models every 24 hours (or after 50+ new events) on freshly labeled attack data |
| Alerting | Telegram bot and Resend-based email dispatch for HIGH/CRITICAL events; MITRE ATT&CK technique mapping on alerts |
| Reporting | PDF (ReportLab), Excel/CSV export for SOC-style incident reports |
| Live Demo | honeycloud-frontend.onrender.com |
| Repository | Anand-Bora-0001/HoneyCloud-X |
The interesting engineering decision here is honest, not glamorous: heavy libraries like TensorFlow/PyTorch are deliberately excluded from the production requirements.txt because Render's free tier OOMs under their weight β so the system falls back cleanly to the Scikit-Learn pipeline in constrained environments instead of crashing.
βοΈ Enterprise MLOps Platform
| Aspect | Detail |
|---|---|
| Domain | AI β’ MLOps β’ ML Engineering |
| Repository | Anand-Bora-0001/Enterprise-MLOps-Platform |
Add your own model-lifecycle/pipeline/deployment details here β same note as above, keep this section in your own words.
π Real-Time Data Engineering Platform
| Aspect | Detail |
|---|---|
| Domain | Data Engineering β’ Big Data β’ Streaming |
| Repository | Anand-Bora-0001/Real-Time-Data-Engineering-Platform |
Add your own streaming architecture/pipeline details here.
π°οΈ Cloud Infrastructure Observability & Monitoring Platform (observability-platform)
A real-time infrastructure observability and incident-response platform built to act as a unified command center for SRE/NOC-style workflows β automated alert triage, incident tracking, ticketing, and maintenance-window scheduling.
| Aspect | Detail |
|---|---|
| Stack | FastAPI, React + TypeScript + Vite, PostgreSQL, Redis, Prometheus, Grafana, Alertmanager, Node Exporter, cAdvisor, Blackbox Exporter |
| Architecture | Split between metric ingestion (Prometheus), visualization (Grafana), and incident routing (Alertmanager β FastAPI webhooks) |
| Scale | Multi-host monitoring with 15s scrape intervals; SLA/MTTR/MTBF tracking at daily/weekly/monthly granularity |
| Security | JWT-based stateless authentication |
| Deployment | Docker Compose for local dev, Kubernetes manifests for HA production |
| Repository | Anand-Bora-0001/cloud-monitoring-dashboard |
Built around a genuinely useful ops problem: turning raw metrics into actioned incidents instead of just dashboards nobody watches. The webhook engine checks active maintenance windows before generating incidents, so scheduled downtime doesn't create alert noise.
π’ Enterprise Digital Transformation Platform
A microservices-based operating system for enterprise workflows β combining BPMN 2.0 workflow automation, a configurable no-code business rules engine, agile/Scrum project management, and practical AI document summarization.
| Aspect | Detail |
|---|---|
| Stack | FastAPI (Python 3.12), React 18 + TypeScript + Vite, PostgreSQL 15 (JSONB), Redis, Celery, MinIO/AWS S3 |
| Architecture | Clean Architecture with strict router β service β repository separation; dependency injection via FastAPI Depends() |
| Scale | SLA escalation engine with time-boxed workflow steps; multi-channel dispatch (Email, Teams, Slack, Twilio SMS) |
| Security | Stateless JWT auth, bcrypt password hashing, granular RBAC (Users β Roles β Permissions) |
| AI | Celery background workers call an LLM to summarize stored documents and help optimize workflow execution |
| DevOps | Docker Compose, Kubernetes (Helm charts), GitHub Actions CI/CD, Prometheus/Grafana monitoring |
| Repository | Anand-Bora-0001/Enterprise-Digital-Transformation-Platform |
The business rules engine is the centerpiece β rules like IF Budget > 1Cr AND Department == 'Finance' β escalate to CFO are configured by end users, not developers. Kafka-based event sourcing and ElasticSearch are on the documented roadmap but not yet built.
ποΈ Enterprise Data Governance & Analytics Platform
A microservices platform for enterprise metadata management, data quality monitoring, and business glossaries β modeled on the workflows of tools like Purview and Collibra.
| Aspect | Detail |
|---|---|
| Stack | FastAPI microservices (catalog, metadata, quality, reporting, analytics services), React + TypeScript, PostgreSQL, Redis |
| Architecture | Repository Pattern, Service Layer, Dependency Injection, event-driven metadata scanning |
| Scale | Automated metadata discovery across PostgreSQL, MySQL, SQL Server, Oracle, MongoDB, S3, CSV/Excel/Parquet |
| Security | JWT + granular RBAC, immutable audit logs, GDPR-oriented PII detection and data classification tagging |
| Data Quality | Deep profiling (null %, duplicate %, distinct %, statistical summaries), configurable validation rules |
| AI | Natural-language data assistant that answers questions like "what datasets contain Customer ID?" by querying catalog metadata |
| Repository | Anand-Bora-0001/Enterprise-Data-Platform |
The steward workflow (Data Owner β Data Steward β Approver β Published) is deliberately modeled on real enterprise data-governance processes β the part of the system that would actually matter to a Chief Data Officer.
π Bulk Certificate Generator
| Aspect | Detail |
|---|---|
| Domain | Flask β’ Automation β’ PDF Generation |
| Stack | Flask, ReportLab, CSV bulk processing, Google Sheets API |
| What it does | Generates certificates in bulk from customizable templates, driven by CSV input β the same automation later extended during the AI Adventures LLP internship |
| Repository | Anand-Bora-0001/Bulk-Certificate-Generator |
πΌ Portfolio Website
| Aspect | Detail |
|---|---|
| Domain | React β’ Portfolio |
| Live Site | anand-bora.vercel.app |
| Repository | Anand-Bora-0001/Anand-Bora |
- Developed and maintained backend applications using Python and Flask
- Built an automated Bulk Certificate Generator with PDF generation and CSV-based bulk processing
- Automated workflows using the Google Sheets API to reduce manual effort and improve operational efficiency
- Enhanced and maintained the Moodle LMS, implementing new features and fixing bugs
- Developed and integrated REST APIs for seamless frontendβbackend communication
- Optimized application performance, debugged issues, and improved code quality
- Implemented GitHub Actions workflows for automated build and deployment
- Collaborated with the dev team using Git, participating in code reviews and feature development
Tech Stack: Python Β· Flask Β· FastAPI Β· REST APIs Β· Google Sheets API Β· Git Β· GitHub Actions Β· HTML Β· CSS Β· JavaScript Β· SQLite/PostgreSQL Β· Pandas Β· ReportLab Β· CSV Processing Β· JWT Authentication
| Project | Description |
|---|---|
| Railway Management System | MySQL + Flask project for railway operations management |
| Cloud-SaaS-System | Python-based SaaS system project |
| Contact-Management-App | Python contact management application |
| Ecommerce-App | Java e-commerce application |
| Recognition | Details |
|---|---|
| Nine-project builder | Shipped nine independent platforms spanning cybersecurity, MLOps, data engineering, observability, and enterprise workflow automation |
| Collaborative engineering | Co-built HoneyCloud-X with a fellow developer, including a live production deployment |
| Industry experience | Python Developer Intern at AI Adventures LLP β shipped automation tooling and LMS features used in production |
| Consistent GitHub activity | Active public repository history spanning backend, full-stack, and applied-ML projects |
To make the snake animation actually render, add a GitHub Actions workflow (
Platane/snk) to your profile repo β see github-contribution-grid-snake for the one-file setup.
learning:
- Kubernetes in production (HA, autoscaling)
- Event-driven architecture with Kafka
- System design fundamentals
building:
- Deeper AI integrations across existing platforms
- MLOps pipelines for model deployment at scale
- Real-time streaming data pipelines
exploring:
- Multi-tenant SaaS architecture
- Cybersecurity automation & SOC tooling
open_to:
- Software Engineering roles
- Backend / Platform Engineering roles
- Graduate programs
- Hackathons and open-source collaboration