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AI Engineer with less than a year in Machine Learning, Deep Learning, and Data Analytics.
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Assessing your cultural and operational fit
AI/ML Engineer with strong expertise in Machine Learning, Deep Learning, and Data Analytics. Experienced in building end-to-end AI solutions, from data preprocessing to model development and deployment. Proficient in Python, NLP, and computer vision, with a proven ability to improve model performance and deliver scalable, real-time intelligent systems. Passionate about solving real-world problems using data-driven approaches.
LBS College of Engineering Kasaragod, Kerala
B.Tech · Electronics and Communication Engineering
August 1, 2021 – June 30, 2025
Luminar Technolab
Data Science Intern
January 1, 2025 – August 1, 2025
India
Hybrid Log Classification System (Regex + Statistical ML + LLM)
January 1, 2025 – June 1, 2026
Developed a hybrid text classification system to automate real-time log analysis and reduce manual monitoring effort by combining Regex-based rule matching, BERT-based statistical learning, and Qwen LLM routing via Groq Cloud for accurate and cost-efficient classification. Built a production-ready FastAPI backend integrated with a Streamlit dashboard for live inference, confidence monitoring, intelligent model routing, and performance analytics using Python, Transformers, and Scikit-learn.
View ProjectCardioSense - AI Cardiac Health Monitoring System
January 1, 2025 – June 1, 2026
Built an AI-powered cardiac monitoring system for ECG anomaly detection, improving model accuracy by 25% through advanced preprocessing and class balancing. Designed a real-time mobile health application with automated alerts, reducing response time for abnormal conditions by 35%.
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a proactive and innovative approach to problem-solving, combining various AI techniques (Regex, statistical ML, LLM) for optimal solutions. The focus on real-world applications (cardiac health, log classification) and performance improvements aligns well with a results-driven culture. The breadth of skills and technologies used, despite limited professional experience, suggests a strong learning aptitude and willingness to explore diverse tools, which is beneficial for dynamic team environments. However, the lack of team-based project descriptions makes it difficult to fully assess collaboration and broader cultural alignment beyond individual contributions.
Soft Skills & Operational Fit
The candidate highlights analytical problem-solving, strong logical thinking, effective communication, adaptability, and leadership. These soft skills are crucial for an AI Engineer role, especially in problem-solving and adapting to new technologies. The project descriptions indicate an ability to work on complex, multi-faceted problems and deliver functional systems, suggesting good operational fit for roles requiring end-to-end development.