AI-Powered Retail Shelf Intelligence: Lead Developer Insight

AI-Powered Retail Shelf Intelligence: Lead Developer InsightAI-Powered Retail Shelf Intelligence: Lead Developer Insight

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Arslan Mehmood

• Jul 10

Shelfr - AI-Powered Retail Shelf Intelligence Platform

I led the development of Shelfr as the Senior Computer Vision Engineer and Lead Developer, taking the platform from the initial idea and system architecture through development, deployment, and production operations.

My work included:

🔹 Designing the complete computer vision and backend architecture 🔹 Building product detection, shelf analysis, OCR, and image-processing pipelines 🔹 Developing APIs and scalable data-processing workflows 🔹 Deploying and managing production systems on GCP cloud servers 🔹 Optimizing model accuracy, processing speed, and infrastructure performance 🔹 Managing production monitoring, reliability, troubleshooting, and ongoing improvements 🔹 Leading technical decisions across AI, backend, cloud infrastructure, and DevOps

The platform converts real-world retail shelf images into structured product and shelf-level insights, helping automate retail auditing, product visibility analysis, and inventory workflows.

#ComputerVision #RetailAI #LeadDeveloper #AIEngineering #GCP #MLOps #Python #CloudEngineering

AI Model Development Machine Learning Engineering Product Data Analytics retailanalytics computervision FastAPI Hugging Face Python

Papa Yusuf

• Aug 2

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Schubert Santos

• Aug 2

If your bots touch a browser/API, I can be the reliability layer: reproduce one failure, add a focused Playwright regression, and leave client-ready evidence. Authorized QA subcontract, US$49: https://contra.com/payment-link/0ThEp1VQ-playwrightapi-failure-triage

Gabriel Gabriel

• Aug 1

Many companies are building AI applications with LLMs but struggle to understand what is happening behind the scenes. Without proper monitoring and tracing, teams cannot identify slow responses, expensive API calls, failed generations, prompt issues, or areas where AI performance can be improved.

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I use modern AI monitoring and development tools including Langfuse, Helicone, LangSmith, Arize Phoenix, OpenLLMetry, OpenTelemetry, Weights & Biases, PromptLayer, Traceloop, and Grafana to build a complete LLM observability system.

My process includes SDK integration, API configuration, trace setup, prompt management, cost tracking, dashboard creation, evaluation workflows, error monitoring, and performance optimization. Whether you are building AI agents, RAG systems, chatbots, or enterprise LLM applications, I will help you reduce AI costs, improve reliability, and gain full visibility into your AI operations.