Job Title: AI Engineer

Location: Markham, ON (Hybrid)

Estimated Duration: 6 Months
 

Position Summary

The primary purpose of the AI Engineer role is to leverage advanced data engineering and AI system architecture skills to design, develop, and deploy innovative AI solutions that address complex business challenges. This role is crucial in transforming raw data into actionable insights and intelligent applications that drive strategic decision-making and operational efficiency.
As an AI Engineer, you will collaborate closely with IT Delivery Leaders, data scientists, and business stakeholders to understand and translate business needs into robust AI and data science applications. You will be responsible for the end-to-end lifecycle of AI models, including data collection, preprocessing, model development, deployment, and continuous monitoring and improvement.

Key Responsibilities

AI Solution Development and Deployment

• Collaborate with teams to understand business requirements.
• Identify opportunities for AI solutions to solve business problems.
• Design, develop, and deploy AI solutions, including classic AI, deep learning, and generative AI.
• Leverage machine learning frameworks such as Scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch for model development.
• Determine appropriate machine learning techniques and algorithms for models, evaluate model performance using appropriate metrics, and make improvements as necessary.
• Design, develop, and maintain the AIOps platform.
• Develop and maintain the codebase for models using AIOps/MLOps tools such as Watson Studio and GitHub, ensuring performance, reliability, and scalability.
• Develop and maintain reports, dashboards, and visualizations to present key insights and trends effectively to stakeholders.
• Develop and maintain model data pipelines to ensure data is up-to-date and accurate.
• Work with data governance and data modelling teams to source and manage model data and processes.
• Ensure data governance and security policies are adhered to.
• Document solution architecture, parameters, and hyperparameters, and integrate models into business processes and applications.
• Collaborate with stakeholders to integrate models into business processes and applications.

Exploratory Data Analysis and Insights

• Conduct EDA to identify relationships and trends in data.
• Identify and address data quality issues that may impact model performance.
• Use statistical techniques to identify significant variables and features.
• Develop data visualizations to communicate findings to stakeholders.
• Work with stakeholders to define success criteria for models.

Data Governance, Security, and Collaboration

• Partner with data governance and data modelling teams to source, manage, and govern model data and related processes.
• Ensure data governance, privacy, security, and compliance requirements are followed throughout the AI lifecycle.
• Collaborate with internal and external stakeholders, suppliers, partners, consultants, and technical contacts.

Project Delivery and Other Duties

• Track and report progress in Jira.
• Clearly document task requirements and completed work.
• All other duties as assigned by manager.

Qualifications

Education and Certifications

• University degree in Computer Science, Engineering, or Mathematics.
• Cloud AI Platform Developer or Architect certification.

Experience and Technical Skills

• At least 3 years of experience in developing data science and AI applications, with strong troubleshooting and problem-solving skills.
• Experience in a data science or AI-related role.
• Over 3 years of experience in developing and deploying AI and machine learning models, including deep learning and generative AI.
• Experience working with large datasets, data mining, and data modeling.
• Knowledge of programming languages such as Python or R.
• Experience with cloud platforms and their applications in AI/ML, such as IBM watsonx, AWS SageMaker, and Azure Machine Learning.
• Understanding of cloud platform services such as AWS S3, Redshift, Glue, EMR; Azure Blob Storage, Data Factory; and IBM CP4D.
• Experience with machine learning frameworks such as Scikit-learn, XGBoost, TensorFlow, Keras, or PyTorch.
• Experience in conducting data analysis and exploratory data analysis to identify patterns and trends.
• Background in SQL experience and data integration/processing concepts, including ETL processes.
• Experience with data processing frameworks such as Apache Spark or Hadoop.
• Experience with data visualization tools such as Tableau or Power BI.
• Experience with MLOps tools and practices, such as MLflow, Kubeflow, or Azure ML, to streamline the deployment and monitoring of AI models.
• Experience in implementing and adhering to data governance policies to ensure data quality and compliance with regulations.
• Proficiency in using version control systems like Git for collaborative development and code management.
• Experience working in Agile development environments, participating in sprint planning, daily stand-ups, and retrospectives.

Competencies and Working Style

• Strong problem-solving skills; simplifies complex issues.
• Effective verbal, presentation, and written communication skills.
• Works well independently and in teams.
• Constant learner, focused on improvement and innovation.
• Actively shares knowledge and best practices.
• Enthusiastic about new technology and self-learning.
• Develops and maintains good working relationships with all members of the team.
• Enjoys team-building approach of management and department, and shares results.