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AI Engineer with less than a year in Deep Learning & Computer Vision
AI and Machine Learning Engineer with hands-on experience at DRDO at Naval Science & Technological Laboratory (NSTL), where RF-DETR Transformer-based detection model was optimized for high-precision small object detection in challenging underwater environments, achieving 75% accuracy. Built and managed complete ML pipelines from quality ocean dataset preparation to model training and fine-tuning in a Dockerized server environment. Complementary experience includes an IEEE research internship on Federated Learning with Explainable AI (Attention-LSTM). Proficient in Python, Deep Learning frameworks, Linux-based server operations, and real-time model training motivated to deliver scalable, efficient, and explainable AI solutions for real-world challenges.
Bonam Venkata Chalamayya Engineering College
Bachelor of Technology · CSE (Artificial Intelligence & Machine Learning)
November 1, 2022 – April 1, 2026
Vagdevi's K.V.R. Junior College
Intermediate · MPC
July 1, 2020 – May 1, 2022
Defence Research and Development Organisation (DRDO)
AI Intern
September 1, 2025 – February 1, 2026
Visakhapatnam, Andhra Pradesh, India
IEEE Signal Processing Society
AI Research Intern
June 1, 2025 – July 1, 2025
India
DriveSafe Vision AI
January 1, 2026 – Present
Engineered a real-time monitoring system using MediaPipe Face Mesh to track 468 landmarks at 30-60 FPS, implementing EAR (Eye Aspect Ratio) and MAR (Mouth Aspect Ratio) algorithms for precision drowsiness and yawning detection. Implemented specialized computer vision algorithms, including Eye Aspect Ratio (EAR) for sleep detection and Mouth Aspect Ratio (MAR) for yawning analysis, to provide instant audio-visual safety alerts. Architected a responsive driver dashboard using Next.js and TypeScript, featuring real-time telemetry visualization via Recharts and advanced distraction tracking through Head Pose Estimation (Roll, Pitch, and Yaw). Engineered critical safety integrations including an SOS emergency alert system and automated PDF telemetry report generation using jsPDF for end-to-end trip safety analytics.
Automated Incident Management for Wrong Order Deliveries (Amazon Use Case)
January 1, 2026 – Present
Designed an end-to-end automated incident management workflow to handle wrong-order delivery scenarios in an Amazon-like environment. Implemented ServiceNow ITSM components including Business Rules, Client Scripts, UI Policies, Notifications, SLAs, and automated assignments. Configured users, roles, approval workflows, and escalation logic aligned with enterprise ITSM best practices. Improved incident resolution efficiency by simulating priority-based routing and SLA-driven escalations, reducing manual intervention in ticket handling.
DriveSafe AI: A Real-Time Browser-Based System
January 1, 2026 – Present
Developed a real-time browser-based AI system to monitor user behavior and detect unsafe driving patterns using computer vision techniques. Implemented a pipeline for capturing live video input, performing frame-level analysis, and identifying risk factors such as distraction and drowsiness. Generated instant alerts and insights based on detected behaviors, enabling proactive intervention to improve driver safety.
View ProjectUnderwater Object Detection
September 1, 2025 – February 1, 2026
Developed an underwater object detection system at DRDO - Naval Science & Technological Laboratory (NSTL), optimizing the RF-DETR Transformer-based model for high-precision small object recognition in challenging marine environments. Performed end-to-end quality dataset preparation by collecting, cleaning, and annotating ocean data to build a robust and domain-specific training dataset. Implemented and managed complete model training on a server within a Dockerized environment, achieving 75% detection accuracy through systematic experimentation and optimization. Gained hands-on expertise in fine-tuning deep learning models for real-time inference, covering every phase from data engineering and environment setup to training, evaluation, and result analysis.
View ProjectCertified Application Developer
ServiceNow
January 1, 2025 – Present
Certified System Administrator
ServiceNow
January 1, 2025 – Present
AI Foundation Certificate
Oracle Certified
January 1, 2025 – Present
Achieved a perfect score, indicating excellent proficiency in the evaluated Python skills.
Strengths
Limitations
Cultural Fit Analysis
The candidate's project portfolio is diverse, ranging from personal initiatives (DriveSafe Vision AI) to academic (DriveSafe AI: A Real-Time Browser-Based System) and professional internships (DRDO, IEEE). This breadth, combined with certifications in ServiceNow and Oracle AI, indicates a strong desire for continuous learning and skill acquisition. The involvement in hackathons and open-source contributions (GSSOC) suggests a collaborative and innovative mindset, aligning well with a dynamic technical culture. The target role of 'AI Engineer' is well-aligned with the candidate's project experience and educational background in AI/ML.
Soft Skills & Operational Fit
The candidate's project descriptions and experience highlight a proactive approach to problem-solving and a focus on real-world applicability. The psychometric test score (277/500) suggests potential areas for development in logical reasoning, work attitude, stress handling, or team collaboration, which could impact operational fit. However, the detailed project work indicates a strong drive and ability to execute complex technical tasks.