Job Responsibilities

• Lead the integration and operationalization of machine learning models, ensuring seamless deployment and scalability.
• Work with Google Vertex AI, TensorFlow, PyTorch, and scikit-learn to build and manage ML pipelines.
• Design and develop recommender systems using ML techniques like embedding-based retrieval, reinforcement learning, transformers, and LLMs.
• Collaborate with data scientists and engineers to integrate recommender systems into customer-facing products.
• Conduct AB testing and iterative optimization using data-driven approaches.
• Manage and implement Vertex AI Feature Store for sharing and reusing ML features at scale.
• Oversee infrastructure needs for ML system deployment, including CPU/GPU, networking, and storage considerations.
• Work with data engineers and scientists to ensure high-quality data for model training and integration.
• Ensure efficient end-to-end data-to-AI pipelines, using BigTable/BigQuery for executing machine learning models.

Job Requirements

• Extensive experience with Google Vertex AI and other cloud-based ML platforms (e.g., TensorFlow, PyTorch, scikit-learn).
• Strong background in MLOps methodologies and deploying machine learning models in production.
• Hands-on experience designing and optimizing recommender systems, including AB testing and iterative optimization.
• Experience in managing feature stores, ensuring scalability, transparency, and security in ML operations.
• Proficiency in collaborating with cross-functional teams, ensuring smooth data integration and efficient ML workflows.
• Familiarity with infrastructure requirements for ML systems (e.g., CPU/GPU resources, networking).

Posted By