Senior Machine Learning Engineer

• Collaborate extensively with Data Scientists, Product Managers, and Backend Engineers to bridge the gap between model development and production systems.
• Lead the architectural design of end-to-end ML systems, from data ingestion and training pipelines to real-time inference and monitoring infrastructure.
• Transform innovative data science prototypes into robust, scalable, and secure production software, taking ownership of the "path to production."
• Drive the adoption of MLOps best practices (CI/CD for ML, model versioning, feature stores) to accelerate the feedback loop for Data Scientists.
• Effectively communicate the complexities of ML systems (e.g., latency vs. accuracy trade-offs) to technical and non-technical stakeholders.
• Build and maintain a strong network across the Data and Engineering organizations to ensure ML systems integrate seamlessly with the wider platform.
• Lead projects, mentor peers, and advocate for engineering excellence within the data science domain.

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