Position: ML Delivery manager
Company: EPAM
Experience required: 3y
English level: upper

The client wanted to build a virtual try-on platform to redefine digital and retail experiences and quickly capitalize on new technology opportunities and emerging customer behaviors & beauty rituals
Business Goals
•Transfer ownership of virtual try-on technology to vendors
•Help brands deliver unique and differentiated beauty experiences to market quickly
•Drive product exploration and conversion online and in-stores
Solution Highlights
•Lean startup approach allowed for results-oriented Deep Learning research
•The custom Deep Learning model helps users to try on makeup in real time. The model is skin tone and gender agnostic and works well with glasses, occlusions, and facial expressions. It provides:
•	Face segmentation
•	Face landmark detection
•	Face alignment detection
•The computer vision-powered AR platform built by EPAM features:
•	Mobile apps with Virtual Try-On module running in real-time (30 FPS) on GPUs and ML co-processors
•	Custom face filters for social apps (e.g., MS Teams or TikTok)
•	Makeup support for visually impaired persons
•	Skin issue diagnostics
•	AI Shade matching
•	Model encryption
•Neural rendering is optimized for cross-platform: iOS / Android / Web
•Technology: PyTorch, PyTorch Lightning, Multi-GPU training using DDP, Azure ML, Fairness AI
Results
•End-to-end solution for easy roll-outs: front-end experience, Beauty Artist Console, SDKs, and integration with Virtual Try-On management backend
•Flexibility: Each brand can create applications with unique features on top of the platform
•Scalability: Platform capabilities serve as building blocks for innovation initiatives