Ramba Bala Satya Srinivas
Software Developer • r***************@gmail.com • +91*******404 • linkedin.com/••••• • drivetube.ai/•••••
Career Objective
Software Developer with 0 years of experience focused on Java and web technologies, plus hands-on Machine Learning and Computer Vision project experience. Built end-to-end AI-powered systems (real-time video threat detection, phishing classification) from data ingestion and model training to deployment-ready pipelines. Seeking an entry-level software engineering role to apply strong Java, SQL, and ML skills to build reliable, testable systems and contribute to product development.
Education
Kalasalingam Academy of Research and Education
B.Tech — Computer Science & Engineering • 2022 – 2026
Aditya Junior College
Class XII — PCM • 2020 – 2022
Ravindra Bharathi School
Class X • 2019 – 2020
Technical Skills
Programming Languages: Java,Python
Web Technologies: HTML,CSS
Frameworks and Libraries: OpenCV,NLTK,Object-Oriented Programming
Databases: SQL,Relational Databases
Data and Analytics: Machine Learning,Natural Language Processing,Computer Vision
Projects
ATM Crime Detection System
Tools Used: OpenCV, Computer Vision, Video Processing, Python, Machine Learning
- Engineered a real-time surveillance application that ingests live ATM video feeds and performs frame-by-frame object detection using OpenCV to identify anomalous customer behavior.
- Developed and trained ML threat models to classify suspicious events and integrated threshold-based logic to trigger automated security alerts for immediate response.
- Designed the end-to-end pipeline from live feed ingestion to alert output, enabling structured testing and QA; implemented techniques to reduce false positives across varying lighting and camera angles.
- Implemented video pre-processing and background subtraction to improve detection accuracy and processing efficiency for continuous streams.
- Built a logging and alerting mechanism to capture detected events and metadata for downstream forensic analysis and audit.
AI-Powered Phishing Detection System
Tools Used: Machine Learning, NLP, Web Scraping, Python, scikit-learn
- Built a multi-vector threat-detection system that classifies phishing URLs, emails, and website content in real time using NLP features and ML classifiers.
- Implemented automated web scraping to curate and label large-scale datasets, improving training set coverage across diverse phishing techniques.
- Extracted feature sets from email and webpage content (text tokens, URL characteristics, domain features) and used NLP preprocessing (tokenization, stemming) to drive classifier inputs.
- Evaluated multiple ML models and selected classifiers based on precision-recall tradeoffs to reduce false positives while maintaining high detection rates.
- Integrated the classifier into a validation pipeline with automated test cases and model output logging to support iterative improvements.
Certifications
Java — CodeChef Certified (Object-Oriented Programming & core Java fundamentals) — CodeChef
SQL — CodeChef Certified (Relational database querying, joins, and schema design) — CodeChef
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