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AI Engineer with less than a year in LLM systems & backend development
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Assessing your cultural and operational fit
8th semester student from UET Lahore (GPA 3.47) with hands-on experience building backend APIs, multi-agent LLM systems, and AI-driven platforms. Proficient in Django, FastAPI, LangGraph, and LangChain. Seeking backend, AI engineering, or junior SE roles where I can contribute to production-grade systems.
University of Engineering & Technology
Bachelor of Science · Computer Science
August 1, 2021 – June 30, 2026
Agentified Marketing Automation Platform
January 1, 2025 – June 1, 2026
Built an AI-driven SaaS platform orchestrating a multi-agent pipeline (Creator, Critic, Strategist layers) using LangGraph to automate full marketing lifecycles across X and LinkedIn. Implemented a Generative Engine Optimization (GEO) pipeline to adapt content structure and phrasing for LLM-based search engines (ChatGPT, Perplexity), increasing content discoverability beyond traditional SEO. Engineered a Style Encoder extracting 24 stylistic features (formality, sentiment, lexical diversity) to ensure brand voice consistency in all AI-generated content. Implemented async task scheduling with Celery and Redis for automated posting and hourly analytics, supporting high-concurrency operations.
NetScope - Network Traffic Analyzer
January 1, 2024 – December 31, 2024
Built a full-stack live network traffic analyzer capturing real-time packets using Scapy with BPF filters, classifying traffic across 6+ protocols (HTTP, HTTPS, DNS, SSH, TCP, UDP). Implemented anomaly detection for port scans, DNS tunneling, and traffic spikes alongside TCP/UDP session tracking with state monitoring and PCAP export for Wireshark compatibility. Delivered WebSocket-based live dashboard updates with real-time charts, DNS activity logs, and per-service traffic breakdown using React and D3.js.
Image Classification with Zero Trust Security
January 1, 2024 – December 31, 2024
Built a full-stack adversarially robust image classification system on CIFAR-10 using a ResNet20 model, maintaining 91.84% accuracy on clean images after adversarial training. Improved model robustness against FGSM adversarial attacks by 30.93% (accuracy from 40.78% to 71.74%), by training on both clean and perturbed examples. Enforced Zero Trust security across the full stack — implemented MFA (TOTP), device fingerprinting, user-specific image encryption, role-based access control, and complete audit logging.
Building RAG Agents with LLMs
NVIDIA
January 1, 2026 – Present
Docker Fundamentals
Frontend Masters
January 1, 2026 – Present
JavaScript: The Hard Parts
Frontend Masters
January 1, 2026 – Present
CUDA Programming
NVIDIA
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive and innovative mindset, tackling diverse challenges from AI-driven marketing to network analysis and secure image classification. The pursuit of certifications from NVIDIA and Frontend Masters indicates a commitment to continuous learning and staying current with industry trends. The competitive achievements further highlight a drive for excellence and problem-solving, which aligns well with a dynamic and challenging AI engineering environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong ability to break down complex problems into manageable components and implement robust solutions. The focus on security, scalability, and real-time data processing suggests a detail-oriented and performance-conscious approach. While direct evidence of communication and teamwork is limited to project descriptions, the comprehensive nature of the projects implies effective planning and execution.