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AI / Machine Learning2025

Digital Wardrobe AI Pipeline

Team Lead — ML-powered fashion recommendation engine

Team Lead
role
4
models
Real-time
pipeline

Exhibit Magazine India Pvt. Ltd.

Led and engineered the complete ML pipeline for the Digital Wardrobe App as Team Lead — integrating image styling models, outfit recommendation engines, and background removal pipelines to create an AI-powered fashion platform. Managed the team end-to-end from architecture through deployment.

PythonFastAPIML ModelsRunPodDockerREST APIs
The Challenge

Problem Statement

Create an intelligent fashion platform that can analyze clothing images, remove backgrounds automatically, suggest outfit combinations, and provide style recommendations — all in real-time from a mobile app — while coordinating a multi-person engineering team.

The Solution

How I Solved It

Led the team in building and deploying multiple ML models: an Image Styling Model for fashion analysis, an outfit recommendation engine using collaborative filtering, and a real-time background removal pipeline. All models deployed on RunPod with a FastAPI serving layer. Managed the full project lifecycle from planning to production release.

Results Achieved

Led the team as Team Lead across the full project lifecycle
Image analysis model deployed in production
Real-time background removal pipeline
Outfit recommendation engine live
Collaboration models for Exhibit Social App

Key Takeaways

1. Invest in real-time inference infrastructure early — latency kills user engagement in ML products.
2. Background removal and style transfer models require aggressive optimization to run affordably at scale.
3. Cross-functional team leadership is as important as technical architecture when shipping ML products.

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