AI Engineer with less than a year in LLM integration & multi-agent systems.
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AI Application Engineer with production experience building LLM-powered systems. RAG-based SRS generator processing raw docs, Built a central orchestrator for a 13-agent academic AI system, and an autonomous SEO pipeline using SERP scraping and vector retrieval. Focused on LLM integration, multi-agent architecture, and RAG. BS Software Engineering, FAST-NUCES 2026.
FAST-NUCES Islamabad
BS Software Engineering
August 1, 2022 – June 30, 2026
Systems Limited
Full Stack Engineering Intern
June 1, 2025 – August 1, 2025
Islamabad, Islamabad Capital Territory, Pakistan
FYP Handbook RAG Q&A System
November 1, 2025 – June 1, 2026
Built a RAG chatbot over a 1,000-page university handbook: FAISS vector search with embedding caching; delivering sub-500ms cached responses and 100-300ms cold generation under load. Implemented rate limiting (10 req/min global, 20 req/hour per-user) and exponential backoff retry logic for stability under load.
Detecting AI-Generated Text
October 1, 2025 – June 1, 2026
Fine-tuned BERT & ROBERTa to detect AI-generated text across 17K+ samples, achieving 99.91% accuracy, 0.9986 F1, & AUC-ROC 0.9999 with robustness testing against paraphrasing; deployed end-to-end Streamlit for training & inference.
Multi-Agent AI Orchestration System
September 1, 2025 – November 1, 2025
Co-engineered a Gemini-powered orchestrator for a multi agent educational AI system, implementing single-call confidence-based intent dispatch and conversational clarification, reducing latency by 50% and LLM costs by 66% via a dual-memory architecture (ChromaDB for semantic retrieval, SQLite for structured state).
SEOmation AI SEO Content Automation
August 1, 2025 – June 1, 2026
Built an end-to-end RAG pipeline that scrapes SERP data from Google, & DuckDuckGo, embeds with Cohere SBERT into Qdrant, and generates SEO-optimized content via Groq LLM; producing content that consistently scores 85-92 on Yoast SEO analysis across WordPress and LinkedIn publishing targets. Engineered an automated publishing scheduler with retry logic, keyword targeting, and multi-vertical audience support for a fully autonomous content workflow.
Generative AI for Software Development
Coursera
June 1, 2026 – Present
AWS Cloud Practitioner
Credly
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong passion for AI engineering, particularly in Generative AI and LLM applications. The diversity of personal and academic projects, ranging from SEO automation to multi-agent systems and AI-generated text detection, indicates a proactive and self-driven learning approach. Their involvement with the Google Developer Student Club and pursuit of certifications like 'Generative AI for Software Development' further highlight a commitment to continuous learning and community engagement. This profile suggests a good cultural fit for a dynamic, innovation-focused AI engineering team.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations, such as optimizing LLM costs and latency, and building resilient systems with retry logic and rate limiting. Their project descriptions indicate an ability to work autonomously and deliver end-to-end solutions. The multi-agent system project suggests collaborative potential, though the resume primarily highlights individual contributions. The candidate's focus on practical application and performance optimization aligns well with operational demands.