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AI Research Engineer with less than a year in AI/ML & Data Science
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Siddharth A Shetty is an aspiring AI Research Engineer currently pursuing an MCA in AI & Data Science, with a strong foundation from a BCA in Data Science. He has hands-on experience in developing full-stack applications, IoT systems, time-series forecasting models, and AI-based aerial disaster detection systems. His technical expertise spans Python, PyTorch, MongoDB, React.js, and various AI/ML libraries, demonstrating a commitment to applying innovative solutions to real-world challenges.
Amrita Vishwa Vidyapeetham
MCA · AI & Data Science
August 1, 2024 – June 30, 2026
Amrita Vishwa Vidyapeetham
BCA · Data Science
August 1, 2021 – June 30, 2024
Marimallapa PU College
12th · Commerce
June 1, 2019 – May 31, 2021
St Josheph's High School
10th
June 1, 2017 – May 31, 2019
Examination System – MERN
June 1, 2026 – Present
Designed and developed a full-stack examination management system with role-based access for Admin, Teacher, and Student users. Implemented JWT-based authentication and authorization to ensure secure access control across different user roles. Automated marks calculation and grade assignment, reducing manual effort in result processing. Developed a dynamic grading table generation module adaptable to different examination structures and grading schemes. Built an interactive student dashboard for viewing examination results and academic performance. Integrated RESTful APIs and optimized database operations for efficient data retrieval and scalability.
IoT-Based Agricultural Water Monitoring and Control System
June 1, 2026 – Present
Developed a smart irrigation system for real-time monitoring of soil moisture and water flow parameters. Integrated sensor data acquisition with automated decision-making logic to optimize irrigation schedules. Enabled remote monitoring and control of water pumps through an IoT-based communication framework. Designed a cloud-based dashboard for visualizing live sensor readings and historical agricultural data. Improved water utilization efficiency by minimizing under-irrigation and over-irrigation conditions. Enhanced accessibility for farmers through remote field monitoring and control capabilities.
Time Series-Based Air Pollution Analysis and Forecasting
June 1, 2026 – Present
Analyzed multi-year air quality datasets containing pollutants such as PM2.5, NO2, and SO2 to identify seasonal and long-term pollution trends. Performed data preprocessing, feature engineering, and exploratory data analysis for air quality assessment. Developed and evaluated time-series forecasting models including ARIMA, SARIMA, and LSTM for AQI prediction. Compared forecasting performance against machine learning models such as Random Forest and XGBoost. Evaluated model effectiveness using metrics including RMSE, MAE, and MAPE. Generated visual analytics and forecast comparisons to support environmental monitoring and decision-making.
AI-Based Aerial Disaster Detection and Analysis System
June 1, 2026 – Present
Developed an end-to-end aerial disaster assessment system for analyzing UAV-captured disaster imagery. Implemented image preprocessing and enhancement techniques to improve image quality and model robustness. Fine-tuned EfficientNet-B3 using transfer learning and 5-fold cross-validation for disaster classification on the AIDER dataset. Integrated Grad-CAM++ to generate explainable visualizations highlighting disaster-affected regions. Implemented YOLOv8n-based human detection for identifying potential survivors in disaster scenarios. Developed a FastAPI backend and React-based interface for inference, visualization, and automated report generation. Achieved a classification accuracy of 98.13% on the AIDER dataset.
Autonomous Navigation Through Traffic Cones and Poles Using RF-DETR and Path Planning Algorithms
June 1, 2026 – Present
Developed a perception and navigation framework for autonomous vehicle movement in environments containing traffic cones and poles. Implemented RF-DETR-based object detection to accurately identify navigation-relevant obstacles. Integrated object detection outputs with path planning algorithms including A* and RRT for route generation. Designed obstacle avoidance strategies to support safe navigation through structured environments. Evaluated navigation performance in simulated scenarios involving dynamic path adjustments and obstacle constraints. Improved route planning efficiency and navigation reliability through perception-guided path optimization.
Python for Data Science
Infosys Spring Board
November 1, 2025 – Present
Programming in Java
NPTEL SWAYAM
April 1, 2025 – Present
Google Cloud Computing Foundations
Google skill boost
March 1, 2025 – Present
Software Engineering Fundamentals - Software Development and Testing
Infosys Spring Board
February 1, 2025 – Present
Data Structures and Algorithms
Infosys Spring Board
February 1, 2025 – Present
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' test, indicating a very strong grasp of the subject matter and related skills. This high score reflects excellent technical accuracy and problem-solving abilities in AI/ML.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in diverse applications of AI, including environmental monitoring, disaster response, agriculture, and autonomous systems. This breadth of interest aligns well with a research-oriented role that values innovation and problem-solving across different domains. The focus on practical, impactful projects suggests a proactive and results-oriented mindset. The MERN stack project also shows versatility beyond pure AI, indicating a willingness to learn and apply different technologies.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a structured approach to problem-solving and a focus on practical application. The 'Examination System – MERN' project suggests an understanding of full-stack development and system architecture, which implies good organizational and project management skills. The psychometric test score of 329/500 suggests average performance in areas like logical reasoning, work attitude, stress handling, and team collaboration, which could be further explored in an interview.
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