Rutika Chougule
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// PRODUCTION DEPLOYMENTS & ARCHITECTURAL CASE STUDIES · SECTION 04

Flagship Systems & Production Impact

Technical architecture breakdowns of production systems, projects, and repositories this candidate has attached to their dossier.

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SYSTEM 01 // PROJECTRole: FEATURED PROJECT

AI Lead Scoring Service

Built an AI-powered lead scoring backend achieving 90% prediction accuracy, reducing manual qualification time by 70%. Developed an asynchronous Spring WebFlux backend processing 10,000+ leads per batch using OpenAI API integration. Implemented a CSV ingestion pipeline handling 6+ lead attributes and a hybrid scoring engine combining rule-based logic with GPT-powered reasoning, achieving sub-1.5s response time. Deployed to Railway with CI/CD via GitHub Actions, implementing secure environment variable management.

TechnologyJava
StackSpring Boot
PlatformWebFlux
Outcomeachieving 90% prediction accuracy, reducing manual qualification time by 70%Self-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 02 // PROJECTRole: FEATURED PROJECT

Real-Time Chat Application

Developed an event-driven, real-time messaging backend using Apache Kafka and WebSocket for instant communication. Reduced overall message latency by approximately 60% through targeted performance optimizations. Secured all API endpoints using JWT authentication and Spring Security configurations.

TechnologyJava
StackSpring Boot
PlatformApache Kafka
OutcomeReduced overall message latency by approximately 60%Self-reported
Metric 02—Pending
Metric 03—Pending
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SYSTEM 03 // PROJECTRole: FEATURED PROJECT

Task Manager REST API

Designed a RESTful Task Manager API with complete CRUD operations for tasks and notes. Implemented a clean, scalable Controller-Service-Repository layered architecture with global exception handling. Optimized the MySQL database schema using proper normalization and indexing strategies.

TechnologyJava
StackSpring Boot
PlatformHibernate
Metric 01—Pending
Metric 02—Pending
Metric 03—Pending
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SYSTEM 04 // PROJECTRole: FEATURED PROJECT

News Opinion Mining

Developed an opinion mining system using Python and ML to analyse user satisfaction and detect content quality issues. Implemented classification algorithms to identify biased, misleading, or low-quality news content. Applied NLP techniques and sentiment analysis to evaluate news credibility and user engagement patterns.

TechnologyPython
StackMachine Learning
PlatformSQL
Metric 01—Pending
Metric 02—Pending
Metric 03—Pending
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// RECRUITMENT & ADVISORY ENGAGEMENTS

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