AI Engineer with less than a year in Generative AI & LLM Applications
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AI Engineer with hands-on experience building production LLM applications, RAG pipelines, and real-time voice AI systems. Proficient in LangChain, LangGraph, and multimodal AI architectures. Deployed scalable AI microservices on Azure, AWS, and GCP. Experienced in data engineering, SQL analytics, and machine learning. B.Sc. Computer Science (Data Science), NED University – Graduated Jun 2026, CGPA 3.66.
NED University of Engineering & Technology
Bachelor of Science · Computer Science (Data Science)
October 1, 2022 – June 1, 2026
KoderLabs
Associate AI Engineer
December 1, 2025 – Present
Karachi, Sindh, Pakistan
Mazik Global
AI Engineer Intern
June 1, 2025 – August 1, 2025
Karachi, Sindh, Pakistan
Bilingual Telehealth Voice Agent
January 1, 2026 – June 1, 2026
Real-time medical triage agent supporting Urdu and English voice with WebRTC live state and LiveKit for low-latency audio streaming. Integrated Twilio programmatic numbers for telephone integration, enabling inbound and outbound voice calls. Implemented advanced RAG knowledge retrieval over clinical protocols; used Uplift AI for high-quality Urdu text-to-speech voices. Self-hosted a 23.5B parameter model on a GCP virtual machine with an L4 GPU; backend built in LangGraph and LangChain. Used OpenAI gpt-40-mini-transcribe for transcription and LangSmith for observability and tracing.
MCP RAG Knowledge Server
January 1, 2026 – June 1, 2026
Built a custom Model Context Protocol (MCP) server exposing RAG-powered semantic search over enterprise documents to AI clients like Claude Desktop and Cursor. Implemented document ingestion pipeline with chunking, FAISS vector indexing, and hybrid retrieval with re-ranking; deployed via Streamable HTTP transport for remote access.
Enterprise Data Warehouse & Analytics System
January 1, 2026 – June 1, 2026
Designed a 3-layer data warehouse (bronze, silver, gold) using SQL Server integrating ERP and CRM data sources. Automated ETL pipelines to extract, clean, and load data with 98% accuracy; reduced manual tasks by 25%. Implemented star/snowflake schemas with null/boundary data quality checks enforced via Git version control.
Deep Research Agent System
January 1, 2026 – June 1, 2026
Built an autonomous deep agents system using LangGraph for multi-step planning, tool use, and self-correcting reasoning loops over complex research tasks. Orchestrated specialized sub-agents with task decomposition, long-horizon memory, and RAG-backed retrieval to synthesize sourced, structured reports.
Multimodal RAG Knowledge Assistant
January 1, 2026 – June 1, 2026
Intelligent document assistant with pgvector semantic search and LangGraph multi-step reasoning pipelines. Applied prompt engineering and context engineering to improve retrieval accuracy across large knowledge bases.
ML-Based Fraud Detection System
January 1, 2026 – June 1, 2026
LightGBM model achieving 99.2% accuracy and F1 Score of 0.934 on highly imbalanced financial transaction data. Applied SMOTE and feature engineering; deployed as interactive Streamlit app for real-time fraud risk prediction.
Introduction to Data Engineering
IBM
June 1, 2026 – Present
Programming in Python
Meta
June 1, 2026 – Present
Go Beyond the Numbers: Translate Data into Insights
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong interest in cutting-edge AI applications, including generative AI, multimodal systems, and autonomous agents, which aligns well with an AI Engineer role. The academic and personal projects, combined with internship and associate engineer experience, indicate a driven individual eager to apply and expand their skills. The use of diverse technologies and cloud platforms suggests an open-minded approach to tools and solutions, fostering a good cultural fit for an innovative team.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project descriptions (e.g., Bilingual Telehealth Voice Agent, Deep Research Agent System). The project diversity and use of various tools suggest adaptability and a proactive learning attitude. The experience in building autonomous agent systems indicates an ability to handle multi-step planning and self-correction, which are valuable for operational robustness.