Machine Learning Engineer - Python

As an ideal candidate for this role, you will be responsible for the following key tasks: - 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 required for this role include: - Solid understanding of MLOps practices, including MLflow, model registries, and feature stores. - Experience with TensorFlow Serving, FastAPI / REST API. - Proficiency in 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). - Familiarity with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure). - Knowledge of data storage systems (SQL/NoSQL) and caching mechanisms (Redis, Memcached). As an ideal candidate for this role, you will be responsible for the following key tasks: - 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 required for this role include: - Solid understanding of MLOps practices, including MLflow, model registries, and feature stores. - Experience with TensorFlow Serving, FastAPI / REST API. - Proficiency in 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). - Familiarity with Docker, Kubernetes, and cloud platforms (AWS, GCP, or Azure). - Knowledge of data storage systems (SQL/NoSQL) and caching mechanisms (Redis, Memcached).