Generative AI Engineer - Toronto, Ontario
1 day ago
Job description
Role: AI/ML/GenAI Developer
Location: Toronto, ON
Full-Time permanent role
- Key Responsibilities
- • Design and implement Generative AI solutions leveraging modern architectures and cloud-native services.
- • Develop and optimize Retrieval-Augmented Generation (RAG) patterns, including data indexing and chunking strategies.
- • Build and maintain data pipelines for large-scale data processing using Azure services such as Databricks, Data Factory, and Storage Accounts.
- • Implement CI/CD pipelines for AI applications, ensuring automated testing and streamlined deployment.
- • Architect and develop microservice-based solutions for GenAI workloads.
- • Collaborate with cross-functional teams to integrate AI models into enterprise systems.
- • Monitor and optimize model performance (accuracy, latency, throughput) and ensure scalability for production workloads.
- • Maintain compliance with security and governance standards in cloud environments.
- Preferred Skillset
- • Generative AI Expertise: Strong understanding of generative AI models, algorithms, and their business applications. Hands-on experience with RAG optimization.
- • Data Engineering: Proven experience in building scalable data pipelines and working with large, complex datasets.
- • Cloud & Azure Proficiency: Deep knowledge of Azure services (Databricks, Data Factory, Storage Accounts) and full-stack Azure development.
- • Programming Skills: Proficiency in Python and SQL for data processing and model integration.
- • Microservice Architecture: Experience in designing and deploying microservice-based solutions.
- • Release Management: Familiarity with CI/CD pipelines for AI applications.
- • Platform Experience: Knowledge of LLM deployment in cloud environments and performance optimization.
- • Collaboration & Communication: Ability to work effectively with multi-functional teams and stakeholders.
- • Adaptability: Comfortable handling changes and delivering results in complex, dynamic environments.
- Nice-to-Have
- • Experience with model monitoring tools and setting up performance metrics dashboards.
- • Familiarity with Azure AI services, ML Ops frameworks, and containerization (Docker/Kubernetes).
- • Exposure to data governance and security best practices in cloud environments
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