Senior Machine Learning Engineer to lead the development of their next-generation personalization systems They are looking for a Senior Machine Learning Engineer to lead the development of their next-generation personalization systems, including their Multimodal Recommendation Engine and emerging generative AI initiatives . Lead the development and optimization of generative AI applications for personalized product discovery, search enhancement, and customer engagement. Leverage cutting-edge GenAI techniques, prompt engineering, and transformer architectures, owning end-to-end development of scalable AI/ML pipelines.

Design, build, and maintain highly scalable, robust, and efficient cloud infrastructure using Google Cloud Platform (GCP) services, including Vertex AI, BigTable, BigQuery, AlloyDB, and Cloud Composer. Develop automation and orchestration of ML pipelines, integrating data ingestion, feature engineering, training, and deployment processes. Collaborate with cross-functional teams to understand their needs and build solutions that improve platform usability, scalability, and the overall development experience.

Optimize data processing pipelines and cloud resources to ensure low-latency, cost-effective operation. Implement monitoring, alerting, and failover strategies to ensure platform reliability. Stay current with industry trends and best practices in cloud engineering, data engineering, and machine learning. 5+ years of experience in Machine Learning engineering , with a focus on GenAI applications for personalization. ~ Strong expertise in GenAI frameworks (LangChain or LangGraph)



and building production-grade ML systems. ~ Proven experience with GCP services (Airflow/Cloud Composer + Vertex AI) and ML infrastructure at scale. ~ Proficiency in Python, SQL , and cloud-native development. ~ Track record of deploying ML models to production at scale. ~ Master's or PhD in Computer Science, Machine Learning, or a related field.

Experience with multimodal deep learning architectures and generative AI models. Expertise in building real-time inference systems.

Experience with distributed computing frameworks (Spark) and big data processing. They are looking for a Senior Machine Learning Engineer to lead the development of their next-generation personalization systems, including their Multimodal Recommendation Engine and emerging generative AI initiatives .

Location: Toronto (1 day/week onsite) Lead the development and optimization of generative AI applications for personalized product discovery, search enhancement, and customer engagement. Leverage cutting-edge GenAI techniques, prompt engineering, and transformer architectures, owning end-to-end development of scalable AI/ML pipelines. Design, build, and maintain highly scalable, robust,



and efficient cloud infrastructure using Google Cloud Platform (GCP) services, including Vertex AI, BigTable, BigQuery, AlloyDB, and Cloud Composer.

Develop automation and orchestration of ML pipelines, integrating data ingestion, feature engineering, training, and deployment processes. Collaborate with cross-functional teams to understand their needs and build solutions that improve platform usability, scalability, and the overall development experience. Optimize data processing pipelines and cloud resources to ensure low-latency, cost-effective operation.

Implement monitoring, alerting, and failover strategies to ensure platform reliability. Stay current with industry trends and best practices in cloud engineering, data engineering, and machine learning. 5+ years of experience in Machine Learning engineering , with a focus on GenAI applications for personalization. ~ Robust expertise in GenAI frameworks (LangChain or LangGraph) and building production-grade ML systems. ~ Proven experience with GCP services (Airflow/Cloud Composer + Vertex AI) and ML infrastructure at scale. ~ Proficiency in Python, SQL , and cloud-native development. ~ Track record of deploying ML models to production at scale. ~ Master's or PhD in Computer Science, Machine Learning, or a related field.

Experience with multimodal deep learning architectures and generative AI models. Expertise in building real-time inference systems.

Experience with distributed computing frameworks (Spark) and big data processing. AI may be used in evaluating candidates.

📌 Senior Machine Learning Engineer to lead the development of their next-generation personalization sy (Toronto)
🏢 S.I. Systems
📍 Toronto