AI Engineer with less than a year in Machine Learning and Data-driven Problem Solving
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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.
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.
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.
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.