name: Biradar Srikanth
role: Backend & Microservices Engineer (Java / Spring) | ML-curious
education: B.Tech, Computer Science & Information Technology @ MLRIT (2023 - 2027)
cgpa: 8.63
focus:
- Designing clean, SOLID, layered backend architectures
- REST APIs & microservices with Spring Boot / FastAPI
- DevSecOps tooling, static analysis & CI/CD pipelines
- Practical ML pipelines with Scikit-learn
currently_exploring:
- Distributed systems & system design
- AWS deployments for microservicesLanguages
Backend & Frameworks
ML / Data
Databases, DevOps & Tooling
| Domain | Proficiency | Details |
|---|---|---|
| Backend Development | Advanced | Spring Boot, Spring MVC, REST controllers, layered service architecture |
| Microservices & APIs | Advanced | FastAPI + Spring Boot inter-service communication over REST |
| OOP & Design Patterns | Advanced | SOLID principles, Strategy pattern, modular architecture |
| Databases | Experienced | MySQL schema design, normalised data models, SQL aggregations |
| Security | Experienced | Role-based access, Spring Security filter chain, stateless JWT auth |
| Machine Learning | Intermediate | Scikit-learn pipelines, SMOTE, model evaluation, feature engineering |
| DevOps & CI/CD | Intermediate | Git, GitHub, Maven, static analysis in CI/CD pipelines |
| Cloud | Certified | AWS |
🛡️ Fairness-Checker — DevSecOps Fairness Platform
A DevSecOps platform tracking SRE on-call fairness via Gini coefficients, with static analysis baked directly into CI/CD.
- 🏗️ Layered Spring Boot + Spring MVC architecture with REST controllers, built on OOP/SOLID principles
- 🔍 AST/CST static analysis (JavaParser, Tree-sitter) integrated into CI/CD to flag code-quality issues at commit time
- 🧠 FastAPI microservice for Python-native ML scoring, communicating with the Spring Boot backend over REST
- 🔐 Role-based access (engineer / manager / admin) enforced via Spring Security filter chain with stateless JWT auth
Stack: Java Spring Boot Spring MVC FastAPI REST APIs JWT JavaParser Tree-sitter
💸 Transaction Reconciliation Engine
A modular Java engine that automatically detects and resolves discrepancies across thousands of financial transactions.
- 🗄️ Normalised MySQL schema detecting 3 discrepancy types (amount mismatch, duplicate, missing entry) across 5K+ transactions per run
- 🧩 Modular parser / validator / resolver components built with the Strategy pattern for plug-in rule extensions
- ⚙️ 95% of flagged records auto-resolved via configurable tolerance thresholds — cutting manual audit time from hours to minutes
- 📊 Parameterised SQL aggregation queries powering audit-trail reports by date, counterparty, and discrepancy type
Stack: Java SQL MySQL OOP Strategy Pattern
📉 Customer Churn Prediction System
An end-to-end ML pipeline that scores customer churn risk in real time via a lightweight API.
- 🔄 Full pipeline — ingestion, feature engineering, training & evaluation — on 10K+ customer records with Scikit-learn
- ⚡ Optimised SQL aggregations for feature extraction (tenure buckets, usage ratios, billing patterns), cutting preprocessing time by 40%
- 🎯 SMOTE oversampling + threshold tuning reduced false negatives by 15%, reaching 82% accuracy (Random Forest, cross-validated)
- 🔌 Predictions exposed via FastAPI for real-time CRM dashboard integration
Stack: Python SQL Scikit-learn SMOTE FastAPI
- 🎤 TEDx MLRIT 2024 — Core organizer for a licensed TED event (150–200 attendees); led end-to-end planning, speaker coordination & on-ground execution
- 🥈 Metaloop 2023 — 2nd Place in a 24-hour AR/VR hackathon, delivering a full working prototype under tight time constraints
- 📦 Club Literati, MLRIT — Logistics Associate (2024–Present): managed procurement for 10+ annual events at 98% stock accuracy, helped drive a 30% YoY rise in sponsorships, and mentored junior associates
