Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI/ML-focused final-year B.Tech CSE student with hands-on experience in building real-time computer vision and NLP projects, including emotion detection, AI chatbots, gesture recognition, and accessibility-focused AI applications. Completed internships in Data Science and Data Analytics, with practical exposure to machine learning, data analysis, ETL workflows, and cloud technologies. Skilled in Python, TensorFlow, PyTorch, OpenCV, SQL, AWS, and business-driven technology solutions.
KIIT University, Bhubaneswar
B.Tech · Computer Science and system Engineering
September 17, 2022 – May 3, 2026
Air Drawing
January 6, 2026 – February 25, 2026
Built a real-time virtual drawing app that tracks 21 hand landmarks via MediaPipe and translates finger movement into canvas strokes. Processes webcam feed at ~30 fps with sub-50 ms latency on a standard laptop GPU. Implemented colour selection, eraser, and clear gestures without any physical input device. Real-World Application: Core technology behind touchless kiosks, AR/VR interfaces, and accessibility tools for motorimpaired users — a growing market projected to exceed $26B by 2027 in gesture-based HCI.
View ProjectAI Chatbot
September 1, 2025 – December 29, 2025
Designed and trained an intent-classification chatbot covering 20+ intent categories using bag-of-words + feed-forward neural network. Achieved ~94% intent classification accuracy on the held-out test set. Deployed as a command-line application capable of handling real-time Q&A. Real-World Application: Directly mirrors enterprise chatbot use cases in customer support and e-commerce, where NLPdriven automation reduces support ticket volume by 30-50%, cutting operational costs while improving response times.
View ProjectEmotify
January 31, 2025 – April 25, 2025
Built a real-time AI system detecting 7 emotion classes from facial images using a CNN trained on the FER-2013 dataset (~35,000 images). Achieved ~88% validation accuracy; integrated with OpenCV for live webcam inference at ~25 fps. Applied data augmentation (flip, rotation, zoom) to address class imbalance and improve generalisation. Real-World Application: Applicable in retail (customer sentiment tracking), mental health platforms (mood monitoring), and HR tech (employee engagement tools) — reducing the cost of manual observation by up to 60% in pilot deployments.
View ProjectCultural Fit Analysis
The candidate exhibits a strong cultural fit for an innovative and impact-driven environment, showcasing a clear passion for applying AI/ML to solve practical problems across diverse domains (emotion detection, NLP, gesture recognition). The emphasis on real-world application and business value aligns well with organizations seeking engineers who can translate technical capabilities into tangible benefits. The breadth of skills and continuous learning through certifications also indicates adaptability and a growth mindset.
Soft Skills & Operational Fit
The candidate demonstrates a proactive and results-oriented approach, consistently linking technical projects to business value and real-world applications. Internship experiences highlight exposure to data wrangling, EDA, and model evaluation workflows. The psychometric test score (66.8%) suggests moderate performance in areas like logical reasoning, work attitude, stress handling, or team collaboration, which could be an area for further development.