Senior Machine Learning Engineer to lead the development of their next-generation personalization systems Our Client is Canada's largest retailer. 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. You’ll work with massive-scale datasets across our retail ecosystem to build sophisticated personalization experience that impact millions of Canadians daily. Duration: 6 months (to start)

Responsibilities:
- 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. Must Haves:
- 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.
- Experience with containerization (Docker) and orchestration (Kubernetes).
- Track record of deploying ML models to production at scale.
- Master's or PhD in Computer Science, Machine Learning, or a related field. Nice to Haves:
- Experience with multimodal deep learning architectures and generative AI models.
- Knowledge of modern recommendation system architectures (transformers, neural collaborative filtering).
- Expertise in building real-time inference systems.
- Experience with distributed computing frameworks (Spark) and big data processing. Our Client is Canada's largest retailer. 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. You’ll work with massive-scale datasets across our retail ecosystem to build sophisticated personalization experience that impact millions of Canadians daily.

Location:
Toronto (1 day/week onsite) Duration: 6 months (to start)

Responsibilities:




- 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 productive 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. Must Haves:
- 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.
- Experience with containerization (Docker) and orchestration (Kubernetes).
- Track record of deploying ML models to production at scale.
- Master's or PhD in Computer Science, Machine Learning, or a related field. Nice to Haves:
- Experience with mu

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