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 Engineer with less than a year in Python, LLMs, and Deep Learning.
AI-focused Computer Science Engineering graduate (2025) with hands-on experience in building intelligent applications using LLMs, RAG, and NLP. Proficient in Python, FastAPI, LangChain, LangGraph, and Model Context Protocol (MCP) for scalable AI-powered backend systems. Experienced in developing end-to-end AI pipelines with embeddings, semantic search, and real-time processing, along with a strong foundation in deep learning and computer vision. Passionate about building production-ready AI solutions with strong problem-solving skills.
CMR College of Engineering & Technology
Bachelor of Technology (B.Tech) · Computer Science Engineering
August 1, 2021 – June 30, 2025
Virtual Gesture Control Mouse and Keyboard
May 1, 2025 – Present
Developed a touch-free human-computer interaction system to control mouse and keyboard operations using hand gestures. Implemented real-time hand tracking using OpenCV and MediaPipe to detect and track hand landmarks accurately. Designed gesture recognition algorithms to convert hand movements into mouse movements, clicks, and keyboard actions. Integrated Autopy to automate system control and improve interaction efficiency and accuracy.
LLM-Based Question Answering System Using RAG (LangChain)
May 1, 2025 – Present
Built a Retrieval-Augmented Generation (RAG) based question answering system using LangChain to retrieve and generate answers from custom documents. Implemented document processing using text splitters, embeddings, and vector stores for efficient information retrieval. Integrated semantic search to retrieve relevant document chunks and provide contextual input to the Large Language Model (LLM). Supported multiple document formats such as PDF and text files for scalable knowledge ingestion.
Intelligent Video Surveillance Using Deep Learning
May 1, 2025 – Present
Designed and developed an automated surveillance system for object detection and human activity recognition in video streams. Built deep learning models using TensorFlow, Keras, and PyTorch to detect objects and suspicious activities. Processed video frames using OpenCV for real-time monitoring and video analysis. Improved monitoring efficiency by reducing manual supervision through automated activity detection.
MCP Certification
Hugging Face
June 1, 2026 – Present
2nd Prize - Inter Project Competition (Social Innovation in Practice)
Unknown
June 1, 2026 – Present
Python Certification
Infosys Springboard
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying AI to solve practical problems, from knowledge retrieval to surveillance and human-computer interaction. This project diversity indicates a broad curiosity and willingness to explore different AI domains. The certifications, including a Python certification and an MCP certification from Hugging Face, show initiative in skill development. The target role of AI Engineer aligns well with the candidate's stated technical skills and project focus. However, without professional experience, it's difficult to fully assess cultural fit in a corporate environment.
Soft Skills & Operational Fit
The candidate lists problem-solving, leadership, time management, adaptability, communication, and creativity as soft skills. While these are valuable, there is no direct evidence from the provided data (e.g., completed tests, work experience) to assess their operational fit or how these skills are applied in a professional setting. The academic projects suggest an ability to execute technical tasks, but collaboration and leadership aspects are not detailed.