Machine learning 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/g RPC 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.
Tensor Flow Serving, Fast API / REST API.
MLOps and CI/CD pipelines.
Experience with scalable deployment architectures.
Strong proficiency in Python and ML frameworks such as Tensor Flow, Py Torch, 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/No SQL) and caching mechanisms (Redis, Memcached).