Freelance Architects in Chandigarh
Freelance Architects in Chandigarh
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Ripul Dhiman
Chandigarh, India
Hey i would love to work with you.
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Hey i would love to work with you.
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Pin on RESIDENTIAL
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22
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HOSTEL BLOCK FOR MY PROJECT
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24
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SITE PLANS OF THESIS
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8
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THESIS PROJECT (NATIONAL INSTITUTE OF FASHION AND TECHNOLOGY)
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9
Architect
(4)
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Aakash Aggarwal
Panchkula, India
AI and Backend Engineer
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AI and Backend Engineer
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Clinical NLP & Patient Management Platform Built an end-to-end clinical web platform used by doctors and clinicians to assess patients, manage medical histories, and track treatment progress. The system allowed clinicians to upload patient reports and symptom data, which were processed through an NLP pipeline using IMO Health terminology, tokenised, and structured into a FHIR-compliant database. Developed backend services and dashboards enabling doctors to view, update, and manage patient records. In contrast, patients could track medications, mark adherence (taken / not taken), and view their complete care journey within the app. The platform was designed with clear separation between clinical workflows, NLP processing, and user-facing applications to support reliability, scalability, and safe iteration in a healthcare context.
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33
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Novus Aurelius – Stock Market SaaS Platform Designed and built the backend infrastructure for a stock market SaaS product, including database schema design, scalable APIs, and core business logic using Django and Django REST Framework. Implemented subscription billing via Stripe, optimized database queries and caching for performance, and deployed the system on AWS with CI/CD pipelines. The result was a stable, production-ready backend supporting paid users and real financial workflows.
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Customer Support AI Assistant (RAG Pipeline) Built a Retrieval-Augmented Generation (RAG) system to support customer-facing queries by grounding AI responses in internal documentation and knowledge bases. The system ingested structured and unstructured support content, generated embeddings, and retrieved relevant context at query time to produce accurate, consistent answers rather than generic model responses. The focus was on building a simple, maintainable pipeline with clear data flow and traceability, making it easy to debug incorrect answers, update knowledge sources, and iteratively improve response quality as documentation evolved.
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Automated AI Document Extraction & Classification Pipeline Built a fully automated document processing pipeline to replace manual review and data entry of logistics documents such as invoices and bills. The system ingested uploaded documents, stored them in object storage, and triggered asynchronous AI workflows via message queues to extract required fields, classify document types, and persist structured outputs directly into the database. The pipeline was designed for high throughput and reliability, eliminating human intervention entirely and significantly accelerating business operations by turning unstructured documents into validated, structured data at scale.
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29
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(2)
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