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Fullstack Engineer with less than a year in ML, Deep Learning, and Full-Stack Development.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science graduate from FAST-NUCES with hands-on experience in ML, Deep Learning, and Full-Stack Development. Built production-ready platforms using Python, React.js, FastAPI, and PostgreSQL. Looking for a software engineering or ML role where I can contribute from day one.
FAST National University of Computer and Emerging Sciences
Bachelor of Science · Computer Science
August 1, 2022 – June 30, 2026
VerifiKar - Crowdsourced News Verification Platform
June 1, 2025 – June 1, 2026
Situation: Misinformation was spreading online with no community-driven mechanism to rank the credibility of news content. Action: Architected and built a full-stack platform from scratch - React.js frontend, FastAPI backend, JWT-based authentication, and a trust-scoring algorithm driven by community votes and user reputation scores. Result: Delivered a secure, fully functional platform demonstrating end-to-end system design, scalable API development, and applied algorithm work in a real-world context.
View ProjectMovie Recommendation System
June 1, 2025 – June 1, 2026
Task: Build a personalized movie recommendation engine integrated with live data and a usable web interface. Action: Developed a Flask web app with TMDB API integration for real movie data, applied collaborative filtering to model user preferences, and visualized recommendation networks using NetworkX. Result: Produced accurate personalized recommendations with clear visual output, demonstrating applied ML, API integration, and database management skills.
View ProjectGraph-Based Collaborative Filtering System
January 1, 2024 – December 31, 2024
Task: Design a lightweight recommendation engine using graph theory to reduce computational overhead compared to neural approaches. Action: Modelled user-item relationships as a bipartite graph and computed cosine similarity across nodes to identify latent user preferences. Result: Achieved competitive recommendation accuracy at significantly lower resource cost, demonstrating strong foundational ML and algorithm design skills.
Cultural Fit Analysis
The candidate's projects demonstrate a proactive approach to learning and applying diverse technologies (React, FastAPI, Flask, ML algorithms). The academic and personal projects show initiative and a drive to build, which aligns with a culture that values hands-on experience and continuous learning. The focus on full-stack and ML projects indicates a broad interest in different technical domains, suggesting adaptability. However, the lack of professional experience means cultural fit in a corporate setting is yet to be proven.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to identify problems, design solutions, and deliver functional systems. The 'VerifiKar' project highlights initiative and problem-solving. However, without direct assessment data, specific soft skills like teamwork, adaptability, or leadership cannot be definitively evaluated.