Security Engineer | DFIR & Threat Detection | Network & Cloud Security
I'm a cybersecurity graduate student at the University of Maryland, College Park, currently pursuing my Master's in Cybersecurity along with a Graduate Certificate in Cloud Engineering. I'm drawn to hands-on security work understanding how systems break, how attackers think, and how to build defenses that actually hold up in the real world.
Outside of coursework, I enjoy writing technical blogs, contributing to open-source security tools, and participating in CTFs to keep learning by doing. I'm actively looking for cybersecurity full-time opportunities in US where I can grow through real-world security challenges and collaborative engineering.
Security Researcher Co-Op
📍 Remote
Cyber Security Intern
📍 Virtual, India
Built a secure cloud AI platform where multiple organizations share infrastructure with fully isolated data — enforced via AWS Cognito, per-tenant S3 buckets, Pinecone namespace scoping, prompt-level guardrails, and end-to-end audit logging for compliance.
Forensically analyzed a compromised VMDK disk image to identify malicious executables, trace HTTP-based C2 communication, and reconstruct a full incident timeline by correlating host artifacts with network traffic.
Deployed a full Elastic Stack SIEM to simulate SOC operations — performing real-time log correlation, threat detection, and incident response, with Google SecOps / Chronicle integration for threat intelligence enrichment.
Modeled 10+ attack scenarios using STRIDE and scored 50+ vulnerabilities via DREAD and CVSS v3.1 for an AI system in critical infrastructure, exposing key limitations of static scoring in AI-driven environments.
Built an LLM-assisted alert triage system using LLaMA 2 to summarize SOC alerts and reduce analyst decision time by 30%, with AI-driven anomaly detection and voice notifications via gTTS to cut incident response time by 25%.
Identified 20+ vulnerabilities across EC2, S3, and VPC using AWS Inspector and GuardDuty, then designed hardened remediations — least-privilege IAM, KMS encryption, VPC segmentation, and automated disaster recovery.
Trained a GAN + Random Forest model to detect malicious users in real-time crowdsourced data. Synthetic data augmentation (10K → 50K records) pushed binary classification accuracy from 78% to 99.4%.
📍 College Park, MD, USA
📍 Visakhapatnam, India
EC-Council · CEH v12
Ethical Hacking
Hack The Box · CDSA
Blue Team
Amazon Web Services · Specialty
Cloud Security
The-SecOps · CSEDP
Social EngineeringSeeking opportunities in AI security, cloud-native security, vulnerability research, and security engineering roles that embed security into production systems. Open to internships, research collaborations, and full-time positions.