Help & Supportcareers@gravityer.com
AI Engineer with less than a year in Machine Learning and Data-driven Problem Solving
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science graduate specializing in machine learning and data-driven problem solving. Experienced in designing and validating end-to-end ML pipelines, optimizing model performance through feature engineering, and conducting structured error analysis. Proficient in handling imbalanced datasets, time-series modeling, and applying performance evaluation metrics to improve prediction accuracy. Passionate about AI systems, scalable data workflows, and contributing to large-scale generative AI model evaluation initiatives.
Sree Narayana Guru College of Engineering and Technology, Kerala
B.Tech · Computer Science & Engineering
August 1, 2021 – June 30, 2025
Luminar Technolab
Data Science Intern
June 1, 2025 – February 1, 2026
Cochin, Kerala, India
AI Benchmarking & Hallucination Detection System
June 23, 2026 – Present
Built an end-to-end LLM evaluation pipeline to benchmark models on relevance, hallucination detection, and response quality using a curated dataset of 15 test prompts. Compared Meta Llama3.2 vs Google Gemma running locally via Ollama; Llama3.2 outperformed in quality (63.5% vs 55.1%), while Gemma showed lower risk of hallucination, results displayed through an interactive Streamlit dashboard.
Medical Condition Classification System
June 23, 2026 – Present
Built a disease prediction ML system on 30,000 health records using XGBoost, achieving 87% accuracy with SMOTE-based class balancing and feature scaling. Compared multiple classification models including K-Neighbors, Decision Trees, Random Forest, and Logistic Regression using accuracy, precision, recall, and F1-score; deployed a real-time risk assessment interface.
Pharmaceutical Sales Performance Analysis
June 23, 2026 – Present
Analysed 68,000+ pharmaceutical sales records across regions, products, and distributors; performed EDA to identify revenue trends and patterns supporting data-driven business decisions. Designed and developed a multi-page Power BI reporting dashboard covering executive summary, distributor customer analysis, and sales team performance with interactive filters by year, month, and team.
Air Quality Prediction System
June 23, 2026 – Present
Conducted EDA on time-series pollutant data (PM2.5, PM10, NO2) to identify trends and outliers; engineered features and applied preprocessing and feature scaling to ensure clean model input. Trained and benchmarked multiple regression models using accuracy and error metrics; selected the best performer for real-time AQI forecasting and documented model comparison results.
Cultural Fit Analysis
The candidate's projects demonstrate a broad interest in data science and AI applications, ranging from LLM evaluation to healthcare and business analytics. This diversity suggests adaptability and a willingness to explore different problem domains, which can be a positive indicator for cultural fit in a dynamic environment. The focus on practical application and model comparison aligns well with an engineering mindset. However, the experience level is entry-level, which might require more mentorship and integration into a senior team.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on diverse problems, from LLM evaluation to medical classification and sales analysis. The academic projects suggest a structured approach to problem-solving and an understanding of end-to-end ML workflows. The internship experience, though future-dated, reinforces practical application of ML techniques. However, without direct interaction or specific examples, it's difficult to assess collaboration, stress handling, or communication clarity beyond written descriptions.