Fayroz Tasnim Rowza@fayroSyrup
3
Events attended
1
Submissions made
Bangladesh
1 year of experience
About me
I’m a B.Sc. Computer Science and Engineering student, majoring in Software Engineering (SWE), working toward a career as a Full-Stack AI Engineer. I focus on building full-stack applications with integrated AI capabilities, combining full-stack development with AI engineering to create practical, intelligent, and scalable applications, working with technologies such as React, Next.js, TypeScript, Node.js, Python, FastAPI, REST APIs, databases, OpenAI APIs, LLMs, RAG, AI agents, and related AI engineering tools. I build hands-on projects to apply these technologies in practice, with an emphasis on understanding the code, architecture, and engineering decisions behind them. I’m seeking internship and research opportunities where I can apply my knowledge, gain practical experience, and contribute to collaborative projects. I have good English communication skills and enjoy working with others to build meaningful solutions. Interests: Full-Stack Development • AI Engineering • LLMs • RAG • Web Development • Applied AI Open to collaboration and opportunities: [email protected]
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Factory Agent: AI Quality & Maintenance Assistant
Factory Agent is an end-to-end AI quality inspection and industrial maintenance assistant designed for modern manufacturing environments. It addresses costly equipment downtime and the safety risks of unverified AI recommendations through multimodal defect detection, retrieval-augmented generation (RAG), evidence-grounded diagnosis, and human-in-the-loop governance. The system accepts machine-part photographs, sensor telemetry reports in CSV, JSON, or TXT formats, and technical inspection documents in TXT or PDF formats, including OCR-based text extraction. For visual inspection, Factory Agent combines an advisory local CNN with AMD Token Factory vision models, including Qwen3.8-Flash-Next and Gemma-4-31B-IT. It evaluates individual model predictions rather than averaging confidence scores, flagging low-confidence results and disagreements as UNVERIFIED for expert review. Its RAG pipeline retrieves relevant maintenance procedures from technical manuals. Primary and fallback language models, MiMo-V2.6-Flash and DeepSeek-V4-Flash, generate diagnostic explanations and repair recommendations grounded in retrieved evidence. The system validates recommendations against source manual text and provides citations identifying relevant manuals and pages where available. A human-in-the-loop interface enables plant managers to review predictions, examine supporting evidence, and approve or reject proposed maintenance actions. Only approved cases proceed to Evolus automation workflows for maintenance work-order generation. Built with Next.js, React, TypeScript, FastAPI, PyTorch, and Docker, Factory Agent supports cloud integrations and an offline mock mode for demonstrations and development without API keys. By combining automation, traceable evidence, and human oversight, Factory Agent aims to make industrial AI safer, more transparent, auditable, and practical.
18 Oct 2026
