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Chaudhry Ahmad

@chaudhryahmad

AI/ML Engineer at Neuralige who developed ReventureGPT’s real-estate RAG system and a property platform that gained 2,500+ subscriptions.

Pakistan

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At Neuralige, I developed ReventureGPT, a real-estate research system that uses YouTube transcripts and social-media content to provide grounded answers. I also developed a production property-analysis platform that contributed to more than 2,500 subscriptions within two months.

Previously, at AiBee, I delivered an enterprise lease-abstraction platform using OCR, LLMs, and RAG, with extraction workflows reaching approximately 93% QA-validated accuracy. At Veritus Labs, I built production RAG and semantic retrieval systems and supported their deployment through APIs and CI/CD workflows.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer

Current

Neuralige

Jun 2024 - Present (2 years 4 months)

Delivered AI/ML, Generative AI, computer vision, analytics, and full-stack engineering solutions for client engagements across real estate and geospatial applications. Developed a domain-specific RAG system for real-estate market research and a production property-analysis platform.

Python RAG Embeddings Vector Databases GPT OpenCV Stereoscopy Depth Estimation Object Detection NVIDIA Jetson PostgreSQL REST APIs Statistical Forecasting

AI Engineer

AiBee

Feb 2022 - May 2024 (2 years 3 months)

Designed and delivered an enterprise LLM-powered lease abstraction platform using OCR, LLMs, embeddings, vector databases, semantic search, RAG, and FastAPI. Built document intelligence pipelines and developed RAG and automated extraction workflows reaching approximately 93% QA-validated accuracy.

Python LLM RAG OCR Embeddings Vector Databases Semantic Search fastAPI REST APIs NVIDIA Jetson Computer Vision

AI Backend Engineer

Veritus Labs

Mar 2021 - Jan 2022 (10 months)

Built production-grade RAG and semantic retrieval systems using LLMs, embeddings, vector databases, and enterprise knowledge repositories. Developed scalable Python pipelines for parsing, cleaning, chunking, embedding, indexing, and retrieving structured and unstructured data.