Sanwal.
Kanvis
Sanwal Bilal

Sanwal Bilal

AI Engineer & Architect | Specialist in Computer Vision, RAG, and Autonomous Agents
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Overview

Junior Machine Learning Engineer with a proven track record of architecting end-to-end AI pipelines. Expert in Computer Vision (YOLO, InsightFace) and LLM orchestration (LangGraph, RAG). Developed production-ready systems for real-time safety monitoring and autonomous reconnaissance. Passionate about bridging the gap between complex AI research and practical, scalable hardware/software integrations.
Islamabad, Pakistan

Experience

Feb 2023

WORK

Junior Machine Learning Engineer

AI Soft (Pvt) Ltd
Islamabad, Pakistan
Developed, deployed, and maintained scalable AI/ML applications using Python, TensorFlow, and PyTorch, improving system performance by 30% and reducing processing time by 25%. Designed and deployed ML models for AI-driven features, achieving 90%+ accuracy across key use cases and enhancing user engagement by 20%. Integrated third-party AI/ML APIs into existing web applications, reducing development time by 40% and improving feature delivery speed. Implemented data-driven optimization strategies, increasing model efficiency by 25% and reducing error rates by 15%. Built and optimized NLP pipelines using open-source LLMs for summarization and information extraction, improving response relevance by 35%. Led NLP and computer vision initiatives, improving team productivity by 20% through collaboration and mentorship.
•Developed, deployed, and maintained scalable AI/ML applications using Python, TensorFlow, and PyTorch
•Designed and deployed ML models for AI-driven features
•Integrated third-party AI/ML APIs into existing web applications
•Implemented data-driven optimization strategies
•Built and optimized NLP pipelines using LangChain and model pruning techniques for high-speed summarization and information extraction
•Led NLP and computer vision initiatives

Key Achievements

→Architected and deployed real-time inference workflows using AWS Lambda and Docker, reducing latency for high-speed detection tasks.
→Improved system performance by 30% and reduced processing time by 25% through serverless optimization.
→Achieved 90%+ accuracy across key use cases and enhanced user engagement by 20%.
→Reduced development time by 40% and improved feature delivery speed.
→Increased model efficiency by 25% and reduced error rates by 15%.
→Improved response relevance by 35%.
→Improved team productivity by 20% through collaboration and mentorship.
→Improved system performance by 30% and reduced processing time by 25%
→Achieved 90%+ accuracy across key use cases and enhanced user engagement by 20%
→Reduced development time by 40% and improved feature delivery speed
→Increased model efficiency by 25% and reduced error rates by 15%
→Improved response relevance by 35%
→Improved team productivity by 20% through collaboration and mentorship
PythonTensorFlowPyTorchNLPLLMsComputer VisionAWS LambdaDockerServerless ArchitecturesDatabricksLangChainModel Pruning

Project Portfolio

PERSONAL

Present

AI-Powered PPE Detection & Reporting System

Architect

Architected a scalable AI pipeline combining YOLO-based computer vision and LLM-based reporting for intelligent safety monitoring, specifically optimized for detecting safety shoes on fast-moving personnel. Engineered real-time inference workflows with optimized frame processing and detection pipelines. Developed automated incident reporting using LLMs, reducing manual reporting effort significantly. Designed modular micro-pipeline enabling seamless integration with edge hardware and cloud systems.
Computer VisionLLMYOLOPythonAWS LambdaDockerRaspberry Pi
Code
PERSONAL

Present

Autonomous AI Reconnaissance System

Architect

Architected an LLM-powered autonomous agent system for OSINT-driven reconnaissance and intelligence gathering. Engineered dynamic tool orchestration using LangGraph, enabling context-aware decision loops. Built scalable multi-tool integration pipeline for real-time data enrichment and structured insight generation. Designed extensible framework supporting cybersecurity research and automated intelligence workflows.
LLMsLangGraphOSINTCybersecurityEthical Hacking Tools
Code
PERSONAL

Present

Real-Time Missing Person Detection with RAG-Based Alerting

Developer

Built end-to-end pipeline: Video Stream → Face Detection → Embedding Generation → Vector Search → Identity Matching. Leveraged InsightFace (buffalo l) for high-accuracy facial embeddings; implemented RAG pipeline for large-scale record matching. Designed automated alert system with multi-channel notifications (SMS & Email) for real-time incident reporting. Achieved ~90% recognition accuracy with minimized false accept/reject rates and near real-time detection-to-alert latency.
Face RecognitionVector SearchInsightFaceRAGSMSEmailDatabricks
Code

Education

Apr 2026

B.S. in Computer Science

University of MianwaliSpecialization in Computer Science
GPA: 3.38

Impact & Recognition

Professional Certifications

Google & HEC Pakistan

AI and Cybersecurity (Hacking by Gen AI)

Skills & Interests

Deep Learning

Python (3.x)

LLMs

Generative AI

PyTorch

NLP

Computer Vision

TensorFlow

Ethical Hacking

OOP

Data Structures & Algorithms

Transformer Models

Model Evaluation

NumPy

Pandas

Scikit-learn

Keras

Hugging Face Transformers

REST API Development

AWS

SQL

Relational Databases

Git

Linux

GCP

Databricks

Docker

Serverless Architectures

Data Visualization

System Monitoring & Alerting

SQLAlchemy (ORM)

Distributed & Eventually Consistent Systems

Microservices

Design Patterns

Message Brokers

Event-Driven Systems

Flask

Django

Dependency Injection

SOLID

Azure

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