ML Engineer

Responsibilities

● Optimize model inference for real-time recommendations.

● Containerize ML models using Docker/Kubernetes.

● Build REST APIs for the recommendation engine.

● Monitor model drift and retraining pipelines.

● Productionize machine learning models for fashion and fit recommendations, ensuring

low-latency inference and high scalability.

● Deploy recommendation models using REST/gRPC APIs for real-time and batch

inference.

● Optimize models for performance, memory usage, and response time in high-traffic

environments

● Implement hybrid recommendation pipelines combining collaborative filtering,

content-based filtering, and contextual signals (season, region, trends).

● Integrate stylist-curated rules and human-in-the-loop feedback into ML-driven

recommendations.

● Support personalization based on body type, height, skin tone, ethnicity, and user style

profiles....