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CDAC-certified software developer with a foundation in Electronics & Telecommunication Engineering and hands-on experience at DRDO — India's premier defense R&D organization. My work sits at the intersection of backend engineering, data processing pipelines, and applied machine learning. Not demos. Not notebooks that only run locally. Systems that survive real workloads. At DRDO, I built operational monitoring infrastructure — reducing downtime, automating telemetry collection, and improving data reliability under hard organizational constraints. That shaped a permanent bias toward measurable outcomes over clever abstractions. I write code that is maintainable by someone else, systems that are observable in production, and models that produce defensible predictions. |
name: Kunal Gujar
alias: KunalG80
location: Pune, Maharashtra
timezone: IST (UTC +05:30)
philosophy:
- Systems over hype
- Ownership over delegation
- Clarity over cleverness
- Impact over impressiveness
status: Open to opportunities
focus: Backend Data ML Engineering |
Open to
Languages
Frontend
Backend & Databases
Cloud, DevOps & Tooling
| Domain | Proficiency | Stack & Detail |
|---|---|---|
| Data Processing & EDA | Expert |
NumPy · Pandas · feature engineering · MATLAB |
| Supervised Learning | Advanced |
Classification · regression · ensemble methods · scikit-learn |
| Churn Prediction & CLV | Advanced |
XGBoost pipelines · survival models · ROI attribution |
| ML Pipelines | Proficient |
Model versioning · evaluation harnesses · reproducibility |
| Data Visualization | Proficient |
Matplotlib · Seaborn · dashboard-grade reporting |
| NLP / Text Processing | Intermediate |
Document classification · tokenization · embedding retrieval |
| Deep Learning | Developing |
PyTorch · TensorFlow fundamentals · feedforward & CNN |
| MLOps Fundamentals | Developing |
Model packaging · REST API serving · monitoring basics |
ChurnGuard AI — Customer Churn Prediction & Retention ROI Engine
An AI-driven churn prediction and retention ROI engine that transforms customer risk signals into revenue recovery strategies and executive-ready reports. Built to bridge the gap between model output and business action — a decision-support system, not just a prediction score.
| Stack | Python · scikit-learn · XGBoost · Pandas · Matplotlib · Seaborn · REST API |
| Scale | Mid-market SaaS and subscription businesses |
| Performance | Calibrated probability outputs with revenue-weighted scoring |
| Security | Local inference — no third-party data exposure |
| Impact | Converts model risk scores into executive ROI recovery narratives |
| Repository | KunalG80/Churnguard-AI |
The system ingests customer behavioral data, runs a calibrated ensemble model, segments customers by churn probability bands, and generates structured retention playbooks with projected ROI. The output is what a CRO reads on Monday morning — not a Jupyter notebook that requires a data scientist to interpret.
Developer Portfolio Website — React + TypeScript Engineering Portfolio
A production-grade developer portfolio built with React, TypeScript, and Tailwind CSS — designed as a professional landing page for showcasing engineering projects, certifications, and technical writing.
| Stack | React · TypeScript · Tailwind CSS · Vite |
| Scale | Single-page application — fully client-rendered |
| Performance | Static-first architecture · optimized bundle · fast TTI |
| Security | No backend · no user data collection · no third-party trackers |
| Impact | Primary professional landing page for recruiter outreach |
| Repository | KunalG80/Developer-Portfolio-Website |
Architected with component isolation and typed props throughout. Demonstrates ability to build maintainable frontend systems beyond the typical portfolio generator — typed interfaces, clean state management, deployment-ready output.
CDAC Study Notes — Computer Science Fundamentals Reference
A structured reference covering core CS domains from CDAC's PG-DAC program — data structures, operating systems, networking, databases, and software engineering principles.
| Stack | C · C# · Markdown |
| Scale | 6+ CS domains with implementation-level examples |
| Impact | Open-source reference for systems-level interview and exam preparation |
| Repository | KunalG80/CDAC-Study-Notes |
Organized by domain with practical code examples, not definitions. Built during CDAC training to be genuinely useful to others navigating the same material.
Software / Systems Engineering Intern · DRDO (Defence Research and Development Organisation) · Pune · 2023 – 2024
Worked on operational infrastructure at one of India's premier defense R&D organizations — system monitoring, process automation, and data reliability improvements in environments where failures have measurable consequences.
- Diagnosed recurring system downtime; reduced failure frequency through root-cause automation
- Built monitoring scripts automating telemetry collection and anomaly flagging across infrastructure
- Improved data accuracy in reporting pipelines by redesigning ingestion and validation logic
- Debugged at hardware-software interface level under strict organizational review processes
| Recognition | Details |
|---|---|
| CDAC PG-DAC Certification | Post Graduate Diploma in Advanced Computing — one of India's most rigorous software engineering programs |
| DRDO Production Systems | Delivered working automation and monitoring tools in a live government defense research environment |
| ChurnGuard AI | End-to-end ML product with executive reporting layer — open source |
| React + TypeScript Portfolio | Full production-grade frontend — typed, deployed, publicly accessible |
| CDAC Study Notes | Open-source CS reference used for systems-level interview and exam preparation |
| Multi-Domain Stack | Engineering breadth across backend, frontend, data, ML, and systems programming |
Oracle
CDAC
NPTEL / IIT
Cisco
HackerRank
learning:
- Spring Boot — production-grade Java backend services
- MLOps fundamentals — model serving, monitoring, drift detection
- System design — distributed systems, caching, message queues
- Docker and container orchestration basics
building:
- ChurnGuard AI v2 — cohort analysis and REST API layer
- Backend service templates — reusable Django + PostgreSQL scaffolding
- Personal knowledge base — structured CS reference system
exploring:
- LLM application engineering — RAG, prompt pipelines, evaluation harnesses
- Time-series anomaly detection for infrastructure monitoring
- Graph-based data modeling for relationship-heavy domains
open_to:
- Software Developer (Backend / Full Stack)
- Data Engineer (Entry to Mid-level)
- ML Engineer (Applied AI / Pipelines)
