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AI Engineer with less than a year in Machine Learning & Test Automation
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Artificial Intelligence and Machine Learning enthusiast with a strong academic foundation in machine learning, deep learning, and data science. Possesses hands-on experience in NLP, ML application development, and AI-driven test automation for embedded systems. Proficient in building, training, and evaluating AI models using Python, and experienced in engineering LLM-powered pipelines and coding agents for automated code analysis and test generation. Comfortable working in remote, fast-paced environments and motivated to contribute to real-world AI automation and intelligent systems.
St. Aloysius Institute of Management & IT, Mangalore
Master of Science · Bio-Informatics
August 1, 2024 – March 1, 2026
Alva's College, Mangalore
Bachelor of Science · Microbiology
June 1, 2021 – July 1, 2024
Aumovio
Software Intern
January 1, 2026 – June 1, 2026
Bengaluru, Karnataka, India
Sense Pharmaceuticals
Intern
January 1, 2024 – March 1, 2024
Bengaluru, Karnataka, India
AI-Assisted Module Test Scenario & Code Generation
June 27, 2026 – Present
Architected an AI-powered system that automatically generates Module Test Scenarios and Test Code in C when a Pull Request is raised, by analyzing the diff between PR and master branches and leveraging an LLM for targeted test case generation. Extended the system for the DTCO (Digital Tachograph) codebase by developing an end-to-end pipeline with a coding agent - engineering prompts and workflows to comprehend the existing MT framework, interpret new feature requirements, and produce framework-compliant test artifacts. Validated all AI-generated test cases in the embedded workspace, ensuring comprehensive coverage across boundary, nominal, and error scenarios while reducing manual test authoring effort.
Disease Risk Prediction Using SNP Data
June 27, 2026 – Present
Developed a supervised machine learning model to predict disease susceptibility using SNP genotype datasets. Implemented data preprocessing, feature selection, and model training using Scikit-learn, followed by cross-validation to ensure reliability. Demonstrated strong generalization with accuracy of 82% and CV mean accuracy of 83%. Achieved an AUC of 90% across evaluation metrics, indicating excellent discriminative performance. Deployed an interactive Streamlit dashboard for real-time data input, prediction, and visualization, making the model accessible for non-technical use.
YouTube Comment Sentiment Analysis
June 27, 2026 – Present
Developed an end-to-end sentiment analysis model to evaluate audience opinions from 10,000+ YouTube comments using Python and NLP techniques. Implemented data preprocessing workflows including text cleaning, stopword removal, and normalization, followed by feature extraction and sentiment classification using the VADER Sentiment Analyzer. Generated visual insights through word clouds and sentiment distribution plots using Matplotlib and Seaborn. Demonstrated the effectiveness of natural language processing for social media opinion mining and audience behavior analysis.
Agentic AI for Developers
LinkedIn Learning
January 1, 2026 – Present
Artificial Intelligence for All
Unknown
April 1, 2025 – Present
Introduction to Artificial Intelligence
Unknown
March 1, 2025 – Present
Introduction to R Programming
Unknown
February 1, 2025 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from AI-assisted test generation for embedded systems to disease risk prediction and sentiment analysis, indicates a broad interest in AI applications. The academic background in Bio-Informatics combined with AI certifications shows a proactive approach to skill development. The internship at Aumovio aligns well with the target role of an AI Engineer, especially in the context of AI automation. The personal projects demonstrate initiative and a practical application of learned skills.
Soft Skills & Operational Fit
The candidate's experience as a Student Representative suggests good communication and organizational skills. The profile mentions comfort working in remote, fast-paced environments, indicating adaptability. The project descriptions show an ability to work independently on complex tasks.