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AI Engineer with less than a year in LLM orchestration & full-stack AI application development.
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Assessing your cultural and operational fit
BS Computer Science student at FAST-NUCES concentrating on agentic AI workflows and low-level system design. Experienced in orchestrating multi-agent LLM pipelines, implementing custom data structures in C++, and managing complex data flows. Focused on building and shipping full-stack AI applications that handle autonomous state management rather than relying on simple prompt wrappers.
National University of Computer and Emerging Sciences (FAST-NUCES)
Bachelor of Science · Computer Science
August 1, 2024 – May 1, 2028
CityMind | AI Urban Intelligence System
January 1, 2026 – June 1, 2026
Developed an AI-driven city simulation on a shared graph, integrating CSP layout planning (AC-3 + MRV), Kruskal MST road optimization, and GA-based ambulance placement. Engineered a real-time emergency routing system with dynamic Dijkstra rerouting and built a crime risk prediction pipeline using K-Means and Random Forest classification. Designed a feature-rich Pygame visualization featuring an isometric 3D view, animated coverage pulses, and stochastic flood event handling.
Smart City Management System
January 1, 2026 – June 1, 2026
Developed a console-based Islamabad city simulation using graphs with Dijkstra's Algorithm for shortest path optimization. Implemented N-ary trees, circular queues, and custom hash tables with separate chaining entirely from scratch without using STL for data management.
UniDesk AI | Automated Triage System
January 1, 2026 – June 1, 2026
Engineered an AI-powered helpdesk agent that autonomously classifies, prioritizes, and resolves university IT tickets. Enforced strict LLM outputs using Pydantic structured schemas, guaranteeing stable API integrations and mitigating data hallucinations. Containerized and deployed the full-stack application via Docker, featuring a responsive dashboard for IT staff to monitor real-time ticket flow.
FitCore
January 1, 2026 – June 1, 2026
Developed a 3-layer desktop fitness tracking application using JavaFX and SQL Server to manage user health metrics and workout schedules. Engineered the MVC system architecture with 8 core GRASP/GoF design patterns, and built a custom DAO layer interfacing with an 11-table relational database for secure, decoupled data persistence.
Multi-Agent Research Assistant
January 1, 2026 – June 1, 2026
Orchestrated 4 specialized AI agents for web research and synthesis using LangGraph, integrating Google Gemini 2.5 Flash and Tavily MCP for concurrent search and markdown generation. Engineered a Human-in-the-Loop architecture with conditional routing, confidence-based quality gates, and LangGraph interrupts for query clarification. Built a real-time streaming pipeline using FastAPI and Server-Sent Events (SSE) to power a responsive React frontend for visual agent state tracking.
GTM Cold Email Personalizer
January 1, 2026 – June 1, 2026
Built a Python-based B2B cold email personalizer using Google Gemini 2.5 with advanced 2-shot prompting to generate highly contextual 3-sentence email openers. Engineered a prompt pipeline with negative constraints to eliminate cliché sales phrases and enforce strict structured JSON outputs for reliable downstream parsing. Integrated the LLM API into a CLI tool and Flask dashboard, enabling production-ready automation of outreach workflows.
Generative AI Course
Planet Beyond
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio demonstrates a strong interest and practical application in AI, particularly in agentic AI workflows and LLM integration, which aligns well with an AI Engineer role. The diversity of personal and academic projects (research assistant, email personalizer, helpdesk system, city simulation) showcases a broad curiosity and ability to apply AI concepts to different problem domains. The focus on building full-stack solutions indicates a holistic approach to development. The candidate is currently a student, which implies a strong learning mindset and adaptability, crucial for cultural fit in a rapidly evolving field like AI.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and independent approach to learning and application. The focus on 'building and shipping full-stack AI applications' and 'autonomous state management' suggests a strong problem-solving orientation and a desire to deliver complete solutions. The academic background, while still in progress, shows a strong theoretical foundation in computer science and AI. However, without direct work experience or psychometric test results, it's difficult to fully assess operational fit, teamwork, or stress handling capabilities.