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.

Build and maintain end-to-end MLOps pipelines including training, validation,

deployment, monitoring, and retraining.

Containerize ML services using Docker and orchestrate deployments with Kubernetes.

Implement CI/CD pipelines for ML models and inference services.

Monitor model performance, drift, bias, and recommendation quality in production.

Design automated retraining workflows based on data freshness and performance

metrics.

Collaborate with Data Scientists to tune ranking, diversity, and relevance metrics.

Qualifications:

Solid understanding of MLOps practices, including MLflow, model registries, and feature stores.

TensorFlow Serving, FastAPI / REST API.

MLOps and CI/CD pipelines.

Experience with scalable deployment architectures.

Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.

Hands-on experience with recommendation systems (collaborative filtering, embeddings, ranking

models).

Experience with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure).

Knowledge of data storage systems (SQL/NoSQL) and caching mechanisms (Redis,

Memcached).

ML Engineer