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AI Engineer with less than a year in LLM-powered Systems & Computer Vision
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AI/ML Engineer with hands-on experience building production-grade LLM-powered systems, agentic pipelines, and computer vision applications. Proficient across the full AI development stack - from data preprocessing and model fine-tuning to multi-agent orchestration and real-time deployment. Delivered chatbots, RAG systems, and voice AI agents. Strong foundation in LangChain, LangGraph, CrewAI, OpenAI Agent SDK. Passionate about building scalable, research-driven AI solutions that solve real-world problems efficiently.
Institute of Space Technology (IST)
Bachelor of Science · Computer Science
November 1, 2021 – January 1, 2026
Fazaia Inter College Nur Khan
FSc · Pre-Engineering
July 1, 2019 – July 1, 2021
TRYSOFT AI
AI Engineer Intern
December 1, 2025 – February 1, 2026
Islamabad, Islamabad Capital Territory, Pakistan
Siber Koza Alpha (NASTP)
AI Engineer Intern
July 1, 2025 – August 1, 2025
Rawalpindi, Punjab, Pakistan
Signal IQ, Adaptive Traffic Signal Controller
January 1, 2026 – June 1, 2026
• Engineered an adaptive traffic signal controller using Python and OpenCV, implementing real-time vehicle detection algorithms that dynamically optimized signal timings - reducing average wait times by ~35% in simulated urban traffic scenarios. • Interfaced Python software with Arduino hardware, achieving 90%+ vehicle detection accuracy validated across extensive multi-condition test scenarios including low-light and high-density traffic environments.
Property Advisor Voice AI Agent
January 1, 2026 – June 1, 2026
• Built a real-time voice AI property advisor using LiveKit, Groq LLM (Llama 3.3 70B), Deepgram STT/TTS, and FastAPI - featuring persistent memory, identity resolution, and dynamic multi-criteria property search over a SQLite database. • Integrated Arize Phoenix with OpenTelemetry for full LLM observability; implemented hallucination detection and DeepEval-based faithfulness testing with concurrency benchmarking.
AI Fitness Trainer
January 1, 2026 – June 1, 2026
• Engineered a real-time AI fitness tracker using YOLOv11-Pose and Python, achieving 12+ FPS inference for exercise detection and rep counting across 10+ exercise types with high precision. • Integrated Groq LLM (Llama-3.3) and Plotly to deliver personalized workout summaries and dynamic performance analytics, with real-time form correction using pose estimation.
ResumeIQ, NLP-Powered Resume-Job Matching API
January 1, 2026 – June 1, 2026
• Developed an NLP-powered resume-job matching API using FastAPI, TF-IDF, Sentence Transformers, and spaCy, performing hybrid semantic scoring, skill gap detection, and structured alignment analysis. • Engineered a multi-stage pipeline with PDF parsing, keyword normalization, and weighted scoring (TF-IDF + embeddings + skill overlap), generating automated recruiter-ready candidate evaluation reports.
Customer Support RAG Chatbot
January 1, 2026 – June 1, 2026
• Built a production-ready retrieval-augmented generation (RAG) chatbot using LangChain, LangGraph, and Qdrant vector database, enabling context-aware semantic search with persistent memory across multi-turn conversations. • Deployed across Slack, WhatsApp, Gmail, and Notion APIs; improved contextual retrieval accuracy by 30% through optimized embedding strategies and structured prompt engineering.
CrewSaaS, Autonomous Multi-Agent Product Manager
January 1, 2026 – June 1, 2026
• Built an autonomous multi-agent SaaS planning system using CrewAI, FastAPI, React, Next.js, and PostgreSQL, with a 6-agent pipeline covering market research, feature mapping, system architecture, UX strategy, and revenue modeling. • Implemented real-time agent orchestration via Redis and WebSockets with GPT-40 as the backbone LLM, enabling end-to-end SaaS product specification generation in under 3 minutes.
Data Science Professional Certificate
IBM
June 1, 2026 – Present
AI Engineer Core Track: LLM Engineering, RAG, QLORA & Agents
Udemy
June 1, 2026 – Present
Build RAG Applications
IBM
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from academic research to personal ventures, indicates a strong passion for AI and a continuous learning mindset. The focus on building practical, real-world solutions aligns well with an innovative and results-driven culture. The breadth of skills and technologies explored suggests adaptability and a willingness to tackle various challenges, which is beneficial for cultural fit in a dynamic AI engineering environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and problem-solving approach, with a focus on delivering measurable improvements (e.g., reducing wait times, improving response consistency, increasing retrieval accuracy). The experience in building production-ready systems within tight timelines (8-week sprint) suggests good operational fit and ability to work under pressure. The academic and personal projects demonstrate strong initiative and self-directed learning.