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.

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.

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 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

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