I'm an AI Engineer and Data Scientist with hands-on experience deploying LLM-integrated systems, REST APIs, and end-to-end ML pipelines. I'm passionate about building production-grade AI solutions where reliability matters as much as accuracy.
Currently completing my Bachelor of Computer Science at the University of Sargodha (2022–2026), I've gained practical experience through internships at Sofnix Pvt. Ltd and Data Zenix, where I designed and deployed conversational AI systems, predictive analytics models, and GCP data pipelines.
I'm particularly interested in AI Engineering and Machine Learning roles where I can continue building systems that bridge research and production.
Experience Highlights
AI Engineering & LLM Systems
At Sofnix Pvt. Ltd (Jul 2024 – Nov 2024), I improved system efficiency by 20% by designing and deploying ML models for predictive analytics using Python and TensorFlow. I also boosted customer engagement by 15% by building conversational AI systems with LLM integration, handling intent classification, context management, and response generation—all connected via microservices architecture.
Data Engineering & Analytics
At Data Zenix (Jul 2025 – Aug 2025), I built an end-to-end data pipeline that surfaced key economic trends—including a linear correlation between GDP growth and external debt, and a 40% rising poverty trend. I also achieved a Car Price Prediction MAE of 1.0 using Decision Tree Regression with systematic feature engineering and hyperparameter tuning.
Technical Skills
Languages: Python (advanced), C++, R, JavaScript, PHP
ML / AI: TensorFlow, Scikit-learn, XceptionNet (CNN), Decision Trees, Regression modeling
LLM & AI Engineering: LLM integration, Conversational AI, Intent classification, REST API deployment
Data Engineering: Pandas, NumPy, GCP (BigQuery, Mage), MySQL, MongoDB, Dimensional modeling
Deployment & Infrastructure: REST APIs, Microservices (.NET Core), Model serving, Data pipeline design
Visualization & BI: Tableau, Interactive dashboards
Notable Projects
UOS Assistant — AI Inbound Calling Agent
Architected and led a four-person team delivering a voice-driven inbound calling agent. Processes calls through a Google STT → FastAPI → RAG pipeline (OpenAI embeddings + PostgreSQL/pgvector) → GPT-4o Mini → Google TTS, enabling natural Urdu-language intent classification and contextually aware responses at production scale.
Wavr — Real-Time Chat Application
Deployed a production real-time messaging application on a MERN stack (React, Node.js, Socket.io, MongoDB) with Firebase Authentication for JWT-based identity and Socket.io for sub-second message delivery, achieving zero-polling live presence tracking across concurrent users.
DeepFake Detection System
Achieved 95%+ face classification accuracy by fine-tuning XceptionNet (CNN) on augmented training data, reducing training time by 60% using transfer learning.
Uber Data Analytics Pipeline
Processed and modeled an Uber-scale trip dataset end-to-end—from raw ingestion to dimensional schema design to orchestrated ETL via Mage, reducing query time on aggregated data by 40%.
Certifications
- Google Data Analytics Professional Certificate (Coursera)
- IBM AI Engineer Professional Certificate (Coursera) — supervised/unsupervised learning, deep learning, LLM development, RAG, generative AI agents, computer vision, ML pipelines on Apache Spark
- Deloitte Australia Data Analytics Simulation (Forage, Feb 2026)
- World Quant University Applied Data Science (8 end-to-end projects)
- Data Zenix Data Science Internship Certification
Interests
I'm passionate about:
- Building production ML systems and LLM applications
- Data engineering and ETL pipeline design
- Computer vision and deep learning
- Cloud platforms (GCP, AWS)
- Open-source AI/ML projects
- Continuous learning in AI and ML