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Software Engineer with less than a year in Python, FastAPI, and AI/ML technologies.
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Assessing your cultural and operational fit
Highly skilled Software Engineer with 0.9 years of experience, specializing in Python and FastAPI for API development, and building Agentic AI systems. Proven ability to optimize database performance, enhance system security through JWT/RBAC, and collaborate effectively with cross-functional teams. Demonstrates expertise in large language models, knowledge graphs, and machine learning, with a strong commitment to delivering reliable, scalable, and innovative software solutions. Academic background includes a Master's thesis on Knowledge Graph Driven Adaptive Learning Systems and successful development of an AI document processing system and a lung cancer detection model.
COEP Technological University
Master of Technology · Computer Engineering
September 1, 2023 – July 1, 2025
Walchand Institute of Technology
Bachelor of Technology · Information Technology
July 1, 2019 – July 1, 2023
Uelement
Software Engineer
July 1, 2025 – Present
India
Knowledge Graph Adaptive Learning System
August 1, 2024 – July 1, 2025
Modeled 250+ concepts and 20+ prerequisite relationships in Neo4j to power an adaptive course recommendation engine achieving 95% accuracy. Increased learner engagement by 35% and improved recommendation precision by 18% through iterative A/B testing across diverse user profiles. Designed REST API layer using FastAPI to expose recommendation endpoints, integrating with frontend and enabling real-time personalized course delivery.
Lung Cancer Detection CNN & Grad-CAM
January 1, 2024 – July 1, 2024
Trained VGG16 on 1,000+ CT scans achieving 98%+ accuracy and 90% sensitivity; applied Grad-CAM for model explainability and clinical interpretability. Improved inference speed by 40% over baseline CNN models with a 3-6% accuracy gain, making the pipeline viable for real-time hospital screening workflows. Preprocessed and augmented a dataset of 1,000+ CT scan images using Python and TensorFlow to improve model generalization and reduce overfitting.
Personalized Learning through Knowledge Graph Driven Adaptive Learning Systems
16th IEEE International Conference on Computing, Communication and Networking Technologies (ICCCNT 2025)
July 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects and current role show a strong interest in both traditional software engineering and emerging AI/ML fields. The diversity of projects (adaptive learning, medical imaging, agentic AI systems) suggests a curious and adaptable individual. Participation in university-level sports indicates teamwork and dedication. The focus on practical applications and measurable impact in projects aligns with a results-oriented culture. The candidate's profile suggests a good cultural fit for a dynamic, tech-driven environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through project work (e.g., optimizing dashboard load time, improving inference speed). Collaboration with product and frontend teams is explicitly mentioned, indicating good teamwork potential. The academic background and project diversity suggest an ability to learn and adapt to new technologies. The current role as a Software Engineer aligns well with the target role, indicating operational readiness.