Machine Learning Engineer

Role: ML Engineer

Location: Bangalore

Department: Data Science

Experience: 2–5 Years

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.