Machine Learning Engineer

Role: ML Engineer

Location:

Department:

Experience:

About Styli Marketplace

Launched in 2019 by Landmark Group, Styli Marketplace is the first e-commerce venture of the group, quickly becoming a leading online destination for fashion and lifestyle across the GCC, including Saudi Arabia, the UAE, Kuwait, Bahrain, and beyond. Styli connects global sellers and creators with millions of fashion-forward customers, offering the latest trends, exceptional value, and convenient services like same-day to 48-hour delivery and flexible payment options. Our mission is to make style accessible, aspirational, and exciting for all, backed by a passionate team fostering a culture of creativity and innovation. At Styli, we aim to revolutionize fashion retail and bring unique experiences to our customers.

Role Overview

Machine Learning Engineer - focused on building and deploying ML models. Primarily deploying machine learning models in production. Preferably- realtime ML models in production

Must-have skills

• Strong Python programming:

  clean, modular code; Git; testing and debugging.

• ML fundamentals:

  supervised and unsupervised learning, feature engineering, model evaluation, overfitting and data leakage.

• Hands-on model development:

  experience training, tuning and evaluating models using scikit-learn and PyTorch or TensorFlow.

• SQL and data pipelines:

  preparing large datasets and building reproducible training pipelines.

• Model deployment:

  serving models through APIs or batch inference; experience with Docker and cloud platforms such as AWS, GCP or Azure.

• MLOps:

  model versioning, experiment tracking, automated training/deployment, and monitoring model performance in production.

• Production engineering:

  optimizing inference latency, throughput, memory usage and reliability.

Preferred skills for fashion e-commerce

• Recommendation systems, ranking, search or personalization.
• Computer vision, image embeddings, product classification or attribute extraction.
• Experience with large product catalogs and customer interaction datasets.