Design, develop, and deploy scalable machine learning models for recommendation systems.
Collaborate with cross-functional teams to gather requirements and define project objectives.
Leverage Merlin Models for effective recommendation engine development.
Implement GPU-based recommender systems for efficient and high-performance recommendations in production.
Apply collaborative filtering, content-based filtering, and matrix factorization techniques to enhance the recommendation engine's performance.
Utilize the Azure Databricks ecosystem and ML Flow for seamless integration, model deployment, and monitoring.
Continuously improve the recommendation engine's performance through experimentation, optimization, and regular updates.
Develop and maintain documentation, including project plans, system architecture, and user guides.
Stay up-to-date with the latest advancements in machine learning, recommendation systems, and industry trends to ensure the company remains at the forefront of innovation.
Requirements:
Bachelor's degree in Computer Science, Engineering, or a related field (Master's degree preferred).
5+ years of experience in machine learning engineering, with a focus on recommendation systems.
Proven experience building and deploying recommender systems at scale in production.
Deep understanding of Merlin Models, GPU-based recommender systems, collaborative filtering, content-based filtering, and matrix factorization.
Proficiency in the Azure Databricks ecosystem and ML Flow.
Strong programming skills in Python or similar languages.
Familiarity with machine learning frameworks, such as PyTorch.
Excellent problem-solving, analytical, and communication skills