You won’t just build models—you’ll own how intelligence shows up in real products. This role sits at the intersection of data, engineering, and decision-making, where models aren’t experiments—they’re expected to perform, scale, and deliver measurable impact.

Key Responsibilities

• Design, develop, and deploy scalable machine learning models across production environments
• Translate ambiguous business problems into structured ML solutions with clear success metrics
• Own end-to-end ML pipelines—from data ingestion and feature engineering to model deployment and monitoring
• Work closely with data engineers to ensure high-quality, reliable data pipelines
• Optimize models for performance, latency, and cost in real-world environments
• Continuously evaluate model performance and retrain pipelines based on drift or evolving data
• Collaborate with product and business teams to align ML outputs with decision-m...