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AI Engineer with less than a year in Machine Learning, IoT and Full Stack Development
Results-driven Computer Science graduate student pursuing an M.Sc. at Blekinge Institute of Technology, with a strong foundation in Machine Learning, IoT systems, and Full Stack Development. Experienced in designing and deploying end-to-end intelligent systems from training deep learning models (BERT, Random Forest, RL agents) to building responsive web interfaces with FastAPI and Flask. Deeply interested in the intersection of AI and embedded/IoT systems, exploring how intelligent algorithms can power real-world connected devices. Adept at working across the full development stack, from data pipelines and model training to API design and frontend delivery. Actively seeking opportunities to build impactful, scalable software that bridges the gap between cutting-edge research and production-ready engineering.
Blekinge Institute of Technology
Master of Science · Computer Science
January 1, 2024 – June 1, 2026
Jawaharlal Nehru Technological University (JNTUH)
Bachelor of Technology · Computer Science
August 1, 2020 – November 1, 2023
Parkinson's Disease Detection
June 24, 2026 – Present
Built a multi-modal ML system using voice parameters and handwriting images (wavy & spiral) to detect Parkinson's disease non-invasively. Trained and compared SVM, KNN, Logistic Regression, and Random Forest; Random Forest achieved 92.3% accuracy on the UCI voice dataset. Extracted HOG features from spiral and wavy handwriting images for classification, achieving 95%+ confidence scores on test samples. Developed a Flask + Streamlit web interface for real-time image upload and voice parameter input to deliver live PD predictions.
Port Traffic Management System
June 24, 2026 – Present
Built a Decision Support System using historical AIS data, achieving 95% accuracy in predicting cargo vessel arrivals. Integrated energy analytics, reducing environmental impact by 20% through consumption optimization. Designed FastAPI + HTML frontend improving data processing speed by 30% with real-time predictions.
CartPole Trainer Reinforcement Learning Agent
June 24, 2026 – Present
Engineered an RL agent using OpenAI Gym, achieving 0.05s average decision time with 95%+ stabilization accuracy. Applied experience replay techniques to improve learning stability and convergence rates.
BERT-QA: Precision Question Answering
June 24, 2026 – Present
Built a BERT-based QA model achieving 86.9% exact match and 93.2% F1 score on the SQuAD 2.0 dataset. Leveraged TPU on Google Colab, cutting inference time to 1.5 seconds per query. Maintained 90%+ accuracy across varied question types in real-time testing scenarios.
Cultural Fit Analysis
The candidate's academic projects show a diverse range of applications for AI, from port traffic management to disease detection and reinforcement learning, indicating a broad interest in the field. Their stated interest in the intersection of AI and embedded/IoT systems aligns well with innovative and forward-thinking environments. The competitive programming hobby suggests a drive for continuous improvement and a challenge-seeking attitude. However, the lack of professional experience means there's no direct evidence of collaboration within a team or adapting to corporate culture.
Soft Skills & Operational Fit
The candidate's profile highlights a results-driven approach and an interest in bridging research with production-ready engineering. Their involvement in competitive programming suggests a disciplined and problem-solving mindset. The academic projects demonstrate an ability to work on complex, multi-faceted problems. However, without professional experience, direct evidence of operational fit, teamwork, and communication in a corporate setting is limited.