Help & Supportcareers@gravityer.com
AI Engineer with less than a year in Machine Learning, Computer Vision, Generative AI, and Data Anal
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 and Data Science graduate with hands-on experience in Machine Learning, Computer Vision, Generative AI, and Data Analytics. Skilled in developing end-to-end AI solutions using Python, TensorFlow, PyTorch, SQL, LangChain, OpenCV, and cloud platforms. Experienced in model development, data preprocessing, deployment, and building scalable AI applications. Seeking opportunities as an AI Engineer, Machine Learning Engineer, or Data Scientist.
Nehru Institute of Engineering and Technology (Anna University)
B.Tech · Artificial Intelligence and Data Science
N/A – June 30, 2026
AI Hardware System for Real-Time Deepfake Detection
June 24, 2026 – Present
Engineered a real-time multimodal AI system to detect deepfake video and voice manipulation. Implemented computer vision (OpenCV) and audio processing (Librosa) for feature extraction. Developed CNN-based models for facial forgery detection and spectrogram-based audio classification. Designed end-to-end pipeline: data capture → preprocessing → model inference → trust score generation. Deployed system on edge hardware for low-latency real-time inference. Trained on public datasets (FaceForensics++, DeepFake Detection Challenge, ASVspoof) and custom collected samples. Performed model training and hyperparameter tuning on cloud GPU (Google Colab / AWS EC2 GPU). Achieved ~90% accuracy, 150-300 ms latency, 15-25 FPS.
Real-Time Object Detection & Counting System (YOLO)
June 24, 2026 – Present
Built a real-time object detection system using YOLO for live video streams. Implemented object tracking and counting using bounding box and line-crossing logic. Integrated OpenCV for real-time video processing and visualization. Optimized detection pipeline for high FPS and low latency. Achieved 90-95% accuracy, 20-40 FPS.
AI Assistant using RAG (Retrieval-Augmented Generation)
June 24, 2026 – Present
Developed an AI chatbot for document-based question answering using RAG architecture. Implemented embedding-based vector search using ChromaDB. Integrated LangChain pipeline with LLM for contextual response generation. Enabled semantic search for accurate and relevant answers. Achieved 85–95% retrieval accuracy, 1-3 sec response time.
Machine Learning with Python
GUVI
June 1, 2026 – Present
Python for Data Science
NPTEL
June 1, 2026 – Present
Artificial Intelligence
Accenture Digital Skills
June 1, 2026 – Present
Large Language Models
NPTEL
June 1, 2026 – Present
AWS Academy Graduate – Machine Learning
AWS Academy
June 1, 2026 – Present
Wipro Talentnext – Data Science
Wipro Talentnext
June 1, 2026 – Present
Data Analyst Professional Certificate
Meta
June 1, 2026 – Present
TCS ION NQT (IT) – 2026
TCS
January 1, 2026 – Present
Cultural Fit Analysis
The candidate shows a strong interest in cutting-edge AI technologies through personal projects and certifications. The diversity of projects (computer vision, NLP, RAG, deepfake detection) indicates a broad curiosity and willingness to explore different domains within AI. The target role of 'AI Engineer' aligns well with the candidate's demonstrated technical skills and project focus. However, the lack of professional experience and team-based projects (beyond a hackathon) limits the assessment of cultural fit in a collaborative work environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and hands-on approach to learning and application. Participation in a hackathon suggests teamwork and problem-solving under pressure. However, without direct work experience, specific soft skills like leadership, conflict resolution, or advanced communication in a professional setting cannot be fully assessed.