Machine Learning Engineer

Job Title: Machine Learning Engineer Location: Remote- Canada Duration: Contract Rate: CAD $100/hr. NOTE : Client is not looking Data Scientist/Data engineer, client is looking pure Machine Learning Engineer, who have conceptual knowledge (ML), Deep Learning Position is remote but candidate open to work EST time zone and sometime go to office for metting Job Description Architect and implement advanced deep learning models for multimodal recommendation systems, processing diverse data types including text, images, user behaviour, item features, offer data, and contextual signals. Lead the development and optimization of generative AI applications for personalized product discovery, search enhancement, and customer engagement.

Expert in leveraging cutting-edge GenAI techniques, prompt engineering, transformer architectures, and own 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, Alloy DB, 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 updated with industry trends and best practices in cloud engineering, data engineering, and machine learning Required Qualifications Master's or PhD in Computer Science, Machine Learning, or related field. 8+ years of experience in machine learning engineering, with a focus on recommendation systems or personalization.

Strong expertise in deep learning frameworks (PyTorch or TensorFlow) and building production-grade ML systems. Proven experience with GCP services and ML infrastructure at scale. Proficient in Python, SQL, and cloud-native development.

Experience with containerization (Docker) and orchestration (Kubernetes). Track record of deploying ML models to production at scale. Preferred Qualifications 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.