Description

You will design, build, and own large-scale recommender systems and generative AI solutions to enhance customer personalization.

This role is on-site.

Responsibilities

• Design and improve recommender systems by combining user behavior, product data, and contextual signals.
• Apply LLMs and generative models to create hybrid systems that enhance personalization and customer-facing features.
• Design and run A/B tests and segmentation-based experiments to measure impact and drive model improvements.
• Build and curate datasets, partnering with engineering to develop scalable data pipelines and MLOps workflows.
• Translate experimental results into actionable insights and communicate technical findings to cross-functional teams.

Required Skills

• 5+ years of experience building deep learning models for large-scale recommender systems.
• Proficiency in TensorFlow or PyTorch for model development.
• Strong skills in Python, SQL, and Spark for data analysis and manipulation.
• Experience working with cloud platforms such as Azure or GCP.
• Applied experience with LLMs or generative AI systems.
• Solid understanding of statistics, experimental design, and exploratory data analysis.

Preferred Skills

• Experience working with Databricks.
• Background in Data Engineering and MLOps practices.

Key Skills

Education

Any Graduate