Help & Supportcareers@gravityer.com
AI Engineer with less than a year in Artificial Intelligence & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Dedicated engineering student with a strong interest in Artificial Intelligence, Agentic AI, and Large Language Models (LLMs). Experienced in building intelligent systems using RAG pipelines, LLM orchestration, and real-world data processing. Currently exploring advanced agentic frameworks like LangGraph, CrewAI, and AutoGen. Seeking internships or entry-level roles to develop scalable AI agents and gain industry exposure as an AI Engineer.
CGC University, Mohali, Punjab
B. TECH – ARTIFICIAL INTELLIGENCE AND DATA SCIENCE · Artificial Intelligence and Data Science
August 1, 2023 – June 30, 2027
Partap World School, Pathankot, Punjab
Senior Secondary School
N/A – May 31, 2022
Partap World School, Pathankot, Punjab
Secondary School
N/A – May 31, 2020
EdiGlobe
AI Intern
June 1, 2025 – July 1, 2025
India
Aura-Blood-Inventory-AI
June 2, 2026 – Present
Developed a hybrid ML + Time-Series forecasting system combining Prophet with deep learning models to model seasonal demand patterns and sudden emergency spikes. Designed an AI-assisted decision support system that recommends optimal redistribution of blood units across hospitals. Integrated LLM-based reasoning layer via LangChain for contextual analytics over structured healthcare databases. Deployed scalable backend services for real-time inference and dashboard analytics.
AI-Meeting-Minutes
June 2, 2026 – Present
Developed an AI-powered meeting assistant that converts recorded meeting audio into structured summaries and actionable minutes. Integrated OpenAI Whisper for high-accuracy speech-to-text transcription of multi-speaker meeting recordings. Utilized Phi-3 LLM to generate concise meeting summaries, extract key decisions, and identify action items from raw transcripts. Designed prompt templates to structure outputs into sections such as Agenda, Discussion Points, Decisions, and Action Items. Built an interactive web interface using Gradio, enabling users to upload audio files and receive formatted meeting minutes in real time. Optimized inference workflow to handle long audio files efficiently by chunking and sequential processing.
AI-Brochure-Generator
June 2, 2026 – Present
Architected an end-to-end AI content generation pipeline combining web scraping, NLP-based summarization, and automated document rendering. Implemented prompt engineering strategies to generate persuasive marketing copy tailored to different industries. Designed modular API endpoints for scalable content generation and document export. Enabled automated transformation of raw website data into structured, print-ready PDF brochures.
Fashion-News-Summarizer
June 2, 2026 – Present
Built an AI-powered fashion news summarization system that automatically scrapes real-time articles from online fashion portals using BeautifulSoup and processes them into structured text data. Designed a preprocessing pipeline in Python to clean HTML content, remove noise, and extract meaningful article sections (title, body, metadata). Integrated a locally hosted LLM using Ollama to generate concise, context-aware summaries, reducing article length by 60-70% while preserving key insights. Implemented prompt engineering techniques to improve summary coherence, factual consistency, and domain relevance. Developed and tested the complete pipeline in Jupyter Notebook, enabling rapid experimentation and evaluation of different summarization strategies. Optimized inference performance by running models locally through Ollama, eliminating external API latency and reducing operational cost.
FIND AI – Retrieval Augmented Intelligent Document Assistant
June 2, 2026 – Present
Developed an AI-powered document intelligence system that enables users to upload organizational documents and interact with them through natural language queries using a Retrieval-Augmented Generation (RAG) pipeline. Designed and implemented a complete RAG pipeline that processes documents, retrieves relevant information using vector similarity search, and generates context-grounded responses using a Large Language Model. Built a multi-document ingestion system supporting PDF and Markdown files, including automated text extraction, preprocessing, and document chunking to improve retrieval efficiency. Implemented semantic embedding generation using modern embedding models to convert text chunks into high-dimensional vector representations for efficient similarity search. Integrated ChromaDB vector database to store embeddings and perform cosine similarity-based retrieval, enabling accurate identification of relevant document segments. Developed a context-aware question answering system by combining retrieved document chunks with LLM inference through Ollama, ensuring responses are grounded in the uploaded knowledge base. Engineered a source citation mechanism that returns the document chunk from which the answer was derived, improving transparency and reducing hallucinations. Built an interactive Gradio-based chat interface allowing users to upload documents, index them into a knowledge base, and query them conversationally. Implemented modular architecture separating document loading, chunking, embedding generation, vector storage, retrieval, and answer generation to improve maintainability and scalability. Optimized the system to run locally on resource-constrained machines by using lightweight embedding models and efficient vector search.
Generative AI Foundations
AWS Academy
June 1, 2026 – Present
Artificial Intelligence
Ediglobe
June 1, 2026 – Present
Introduction to MongoDB
MongoDB
June 1, 2026 – Present
Edge Academia
Ericsson
June 1, 2026 – Present
Database Management
Oracle Academy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and practical application in various AI domains, including healthcare forecasting, document intelligence, meeting summarization, content generation, and news summarization. This diversity, coupled with an internship in AI and ML, indicates a proactive and engaged individual. The target role of AI Engineer aligns well with the candidate's demonstrated skills and project focus. The candidate is still an undergraduate, which explains the academic focus of projects and limited professional experience. Their involvement in university clubs (Vice President, Ex-Treasurer) suggests leadership potential and a willingness to contribute to a team environment.
Soft Skills & Operational Fit
The candidate's resume highlights soft skills such as public speaking, time management, decision-making, and strategic thinking. While these are valuable, there is no direct assessment data to validate their proficiency in a professional operational context. The project descriptions suggest an ability to work on complex problems and optimize solutions, which aligns with operational fit for an AI Engineer role.