Help & Supportcareers@gravityer.com
AI Engineer with less than a year in AI/ML Model Development
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Final-semester M.Tech Data Science & Analytics student with hands-on expertise in Python, machine learning, and AI model development. Research-driven background with a focus on hybrid ML architectures achieving 98%+ accuracy. Eager to contribute to real-world AI engineering challenges through internships and collaborative development.
Lovely Professional University
M.Tech · Data Science & Analytics
August 1, 2024 – June 30, 2026
Lovely Professional University
B.Tech · Computer Science & Engineering
N/A – June 30, 2024
Online Retail Sales Analytics Dashboard
June 1, 2026 – Present
Created an interactive Power BI dashboard analyzing 2024 online retail sales data across product categories. Built DAX measures for YoY growth, revenue trends, customer segmentation, and regional performance KPIs. Delivered actionable business insights through drill-through pages and dynamic slicers.
AI Automation Freelance Tool (SaaS Prototype)
June 1, 2026 – Present
Prototyped a niche SaaS tool using Claude API to automate repetitive business workflows for SMEs. Implemented structured prompt engineering with role-based and chain-of-thought techniques for reliable output.
Cardiovascular Disease Prediction System
June 1, 2026 – Present
Developed a multi-model ensemble system for predicting cardiovascular risk using patient health records. Applied feature engineering, SMOTE balancing, and hyperparameter tuning to optimize prediction accuracy. Formatted research findings for IJEER journal submission with structured methodology and result analysis.
AI-Powered Chatbot with RAG Pipeline
June 1, 2026 – Present
Built an intelligent Q&A chatbot using Retrieval-Augmented Generation (RAG) for domain-specific knowledge queries. Integrated FAISS vector store for semantic document retrieval and OpenAI API for response generation. Deployed via Streamlit with a clean UI, enabling real-time user interaction with custom knowledge bases.
M.Tech Thesis - Early-Stage PCOS Detection
January 1, 2024 – January 1, 2025
Designed a hybrid ML pipeline for early-stage PCOS detection using clinical and hormonal biomarker datasets. Achieved 98.75% classification accuracy. Key breakthrough: KNN imputation with 1.2× oversampling, resolving class imbalance and unlocking peak model performance. Evaluated and compared Random Forest, SVM, XGBoost, and Gradient Boosting models using cross-validation and AUC-ROC. Contributed to healthcare AI research findings structured for academic publication in a peer-reviewed journal.
Ethical Hacking (TryHackMe / HackTheBox)
TryHackMe / HackTheBox
June 1, 2026 – Present
Prompt Engineering: Role prompting, few-shot learning, chain-of-thought, structured XML outputs
Unknown
June 1, 2026 – Present
Power BI Data Analytics - dashboarding, DAX, and data storytelling
Unknown
June 1, 2026 – Present
AI Engineering Roadmap: Python Core → Pandas/NumPy → REST APIs → Prompt Engineering → LangChain → RAG
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects in healthcare AI and personal projects like the RAG chatbot and SaaS prototype demonstrate a strong interest in applying AI to diverse domains. The pursuit of certifications in AI engineering and ethical hacking indicates a proactive learning attitude and broad technical curiosity. While the projects are varied, they are primarily academic or personal, and the lack of professional experience means cultural fit in a corporate setting, especially regarding collaboration and agile methodologies, is yet to be fully assessed.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a research-driven and problem-solving approach, particularly in healthcare AI. The focus on academic publications suggests attention to detail and structured methodology. The freelance tool prototype shows initiative in applying AI to business workflows. However, without direct work experience, operational fit regarding team collaboration, project management, and handling real-world production constraints is unproven.