Artificial Intelligence Engineer

Role: GenAI Engineer

Location: Toronto, ON

Employment Type:-Full-Time permanent

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) andensure 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.