Lead the design and execution of scalable, high-performance machine learning pipelines for online and offline feature engineering

Drive the development and tuning of advanced machine learning models, primarily utilizing Python and TensorFlow

Optimize and manage the deployment of inference pipelines tailored for real-time, low-latency applications such as player telemetry

Guide and standardize machine learning workflow management processes using MLflow across the team

Supervise and expand data integration pipelines via ETL/ELT processes on Databricks

Ensure reliability, debugging, and continuous performance tuning of production-grade ML systems

Establish scalable and automated methodologies for feature engineering and durable model deployments

Collaborate effectively with senior stakeholders to align machine learning solutions with organizational objectives

Strategize effective utilization of Databricks for efficient dataset management and computational workflows

Set and uphold industry-leading best practices for scalable, maintainable, and efficient ML systems

5+ years of experience building scalable machine learning pipelines and production-grade workflows

Minimum of 1 year of leadership experience in relevant roles

Demonstrated expertise in Databricks, MLflow, and TensorFlow in professional applications

Strong proficiency in Python for advanced machine learning and data engineering

Solid background in ETL/ELT platforms with significant experience in large-scale integration processes

Deep familiarity with real-time processing systems optimized for low latency

Proven skills in designing and scaling workflows for both online and offline feature engineering

Expertise in managing ML model lifecycles and optimizing pipelines

Capability to deliver scalable, high-performance solutions in multifaceted and evolving environments

Excellent command of written and spoken English (B2+ level)

Comprehensive understanding of recommender systems with an emphasis on enhancing content discoverability and engagement

Proficiency in deploying machine learning systems within AWS or comparable cloud infrastructure

Background in real-time analytics coupled with telemetry-driven data solutions