EY Lead Data Scientist AI Centre Excellence 

Canada, Ontario, Toronto 

393582447

Your key responsibilities

• AI Service Orchestration: Design, build, and manage orchestration workflows for AI and ML services, ensuring smooth integration and automation of end-to-end processes.
• API Development: Develop, document, and maintain APIs for AI/ML services using Python frameworks such as FastAPI, Django, or Flask.
• ML Model Integration: Integrate machine learning models into scalable, production-grade environments; ensure seamless interaction between APIs and underlying ML models.
• Cloud Functions & Deployment: Leverage Azure Functions and other serverless/cloud-native tools to deploy and manage ML services efficiently.
• System Scalability: Architect solutions with scalability and reliability in mind, employing best practices in software engineering and cloud deployment.
• Collaboration: Work cross-functionally with data scientists, engineers, product managers, and business stakeholders to translate requirements into robust technical solutions.
• Continuous Improvement: Participate in code reviews, contribute to team knowledge-sharing, and help maintain an agile, high-performance engineering culture.
• Documentation: Produce clear, comprehensive technical documentation for code, APIs, and deployment processes.
• Security & Compliance: Adhere to organizational security best practices and ensure all solutions meet internal compliance, accessibility, and privacy standards.

Skills and attributes for success

• Education: Bachelor’s or Master’s degree in Computer Science, Information Technology, or a closely related technical field.
• Experience: 4-6 years of relevant, hands-on engineering experience in AI/ML systems, preferably in enterprise or cloud environments.
• Programming & Frameworks: Proficient in Python; solid experience with FastAPI, Django, or Flask for API development.
• API Design: Strong understanding of RESTful API design, versioning, testing, and security.
• ML Model Integration: Experience deploying and integrating machine learning models into live environments; familiarity with common ML frameworks (scikit-learn, PyTorch, TensorFlow, etc.).
• Cloud & Serverless: Practical experience with Azure Functions; knowledge of cloud architecture and best practices in Azure (or similar platforms like AWS Lambda or Google Cloud Functions).
• Service Orchestration: Exposure to orchestrating AI pipelines and workflow automation (e.g., using Airflow, Azure Data Factory, or similar tools) is an asset.
• Scalability & Performance: Demonstrated ability to design systems for scalability, reliability, and performance in distributed environments.
• Problem-Solving: Ability to tackle complex technical challenges and develop innovative solutions in a fast-paced environment.
• Collaboration: Strong interpersonal and collaborative skills; ability to communicate technical concepts clearly to non-technical audiences.
• Documentation & Best Practices: Commitment to writing maintainable, well-documented, and tested code.
• Communication: Excellent written and verbal communication skills, with the ability to convey technical concepts clearly to diverse audiences
• Continuous Learning: Willingness to stay current with new technologies, frameworks, and best practices in AI/ML engineering.