ML Engineer with less than a year in Web Development & Predictive Analytics
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ML Engineer specializing in end-to-end production systems across NLP, Computer Vision, and predictive analytics. Ships containerized, API-served ML pipelines on AWS EC2 with sub-100ms inference latency. Built and deployed 4 live systems and 17+ projects spanning classification, recommendation, and real-time video intelligence.
Konkan Gyanpeeth College of Engineering
B.E. · Computer Science
August 1, 2022 – June 30, 2026
Prodigy Info Tech
Web Developer Intern
February 1, 2024 – March 31, 2024
India
Netflix Customer Churn Prediction System
June 23, 2026 – Present
Engineered 30+ behavioral and subscription features (inactivity streaks, plan tier, engagement rate) using domain-driven selection with cross-validation, directly modeling signals tied to subscriber revenue loss Trained XGBoost classifier with ensemble tuning achieving 88% accuracy / 91% recall — recall-optimized to minimize missed churners and maximize retention campaign coverage Layered SHAP explainability to surface the top 3 churn drivers per user, transforming black-box predictions into actionable, stakeholder-readable retention signals Deployed modular inference service via FastAPI + Docker on AWS EC2 at ~100ms end-to-end latency; enforced clean separation of preprocessing, model, and API layers
View ProjectReal-Time Face Recognition System
June 23, 2026 – Present
Built a live identity verification pipeline using deep face embedding extraction and L2 distance-based matching, achieving 90-95% recognition accuracy across variable lighting and partial occlusion Optimized real-time video inference to 30-50ms per frame with simultaneous multi-face detection; deployed via Streamlit and WebRTC with client-side inference — no dedicated inference server required
View ProjectEmail/SMS Spam Detection System
June 23, 2026 – Present
Designed full NLP classification pipeline: regex cleaning → NLTK stemming → stopword removal → TF-IDF vectorization → Naive Bayes inference, achieving 96%+ accuracy with balanced precision-recall Persisted trained artifacts for session-consistent predictions; exposed classifier via FastAPI REST at 80-120ms latency and deployed Dockerized stack to AWS EC2 and Render
View ProjectMovie Recommendation Engine
June 23, 2026 – Present
Architected content-based recommender over 5,000+ titles using a precomputed TF-IDF cosine similarity matrix; structured system into distinct data, model, and API layers following scalable service design patterns Delivered personalized recommendations at <100ms response time with real-time TMDB API poster enrichment; deployed FastAPI backend + interactive Streamlit frontend publicly on Render
View ProjectCultural Fit Analysis
The candidate's portfolio showcases a diverse range of ML projects (churn prediction, face recognition, spam detection, recommendation engine) across different domains (predictive analytics, computer vision, NLP). This diversity, coupled with the use of various technologies and deployment platforms (AWS EC2, Render), indicates adaptability and a broad interest in applying ML solutions. The projects are well-documented with clear metrics and architectural considerations, suggesting a results-oriented and structured approach. The candidate's experience level is entry-level, but the projects demonstrate initiative and a proactive learning attitude, which are positive indicators for cultural fit in a dynamic environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong focus on practical application, performance optimization, and deployment, which aligns well with operational roles. The emphasis on 'shipping containerized, API-served ML pipelines' and 'enforced clean separation of preprocessing, model, and API layers' suggests an understanding of robust software engineering practices in an ML context. The web developer internship, though brief, shows experience in team environments and Git workflows.