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Building practical machine learning systems beyond notebooks. Interests: NLP, Computer Vision, ML engineering.
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Assessing your cultural and operational fit
C. V. Raman Global University, Bhubaneswar
Computer Vision
June 12, 2026 – Present
realtime-server-monitor
May 10, 2026 – Present
Real-time server monitoring and downtime analytics system using Python, Streamlit, SQLite, and ICMP/TCP monitoring.
View ProjectRiskForge
January 10, 2026 – Present
Most machine learning projects stop at model training and evaluation. However, in real-world systems, models must be: Served reliably Validated against bad inputs Integrated with user interfaces Scalable and maintainable RiskForge addresses this gap by showcasing end-to-end ML engineering practices.
View ProjectReal-Time-Face-Emotion-Recognition
January 9, 2026 – Present
CNN (Keras / PyTorch) OpenCV FER2013 dataset
View ProjectCredit-Risk-Loan-Default-Prediction-System
January 9, 2026 – Present
Financial institutions face significant losses due to loan defaults. Traditional rule-based systems fail to capture complex borrower behavior. This project builds an ML-based credit risk prediction system that: Predicts probability of loan default Handles imbalanced real-world data Explains model decisions using Explainable AI (XAI)
View ProjectIntelligent-Resume-Screening-System
January 9, 2026 – Present
Automatically analyzes resumes Compares them with a job description Ranks candidates based on relevance using NLP techniques
View ProjectFace-detection-System
November 20, 2025 – November 20, 2025
Face-detection-System — GitHub repository
View ProjectData-Algo-Tester
November 15, 2024 – Present
The project aims to determine the most efficient algorithm for multi-class classification problems. Key Features 📊 Evaluation of Algorithms: Compare k-NN and SVM on multiple performance criteria. ⚙️ Metrics Focus: Accuracy, speed, and computational resource usage.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and heavily focused on machine learning and computer vision, which aligns with the target role. However, the lack of diverse project types or team-based experiences makes it difficult to fully assess cultural fit beyond technical alignment. The experience level is listed as 0, suggesting a very junior profile despite the project breadth.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate an ability to articulate technical concepts, but no direct assessment of collaboration, problem-solving under pressure, or communication style in a team setting is available.