Cloud AI Application Architect

About Kinaxis

Elevate your career journey by embracing a new challenge with Kinaxis. We are experts in tech, but it’s really our people who give us passion to always seek ways to do things better. As such, we’re serious about your career growth and professional development, because People matter at Kinaxis.

In 1984, we started out as a team of three engineers. Today, we have grown to become a global organization with over 2000 employees around the world, with a brand-new HQ based in Kanata North in Ottawa. As one of Canada’s Top Employers, we are proud to work with our customers and employees towards solving some of the biggest challenges facing supply chains today.

At Kinaxis, we power the world’s supply chains to help preserve the planet’s resources and enrich the human experience. As a global leader in end-to-end supply chain management, we enable supply chain excellence for all industries, with more than 40,000 users in over 100 countries. We are expanding our team as we continue to innovate and revolutionize how we support our customers.

Location: our office in Ottawa, Canada (Hybrid)

About the role

The Cloud AI Application Architect, as a seasoned professional with in-depth experience, will provide technical expertise in designing, building, and maintaining the AI Cloud infrastructure at Kinaxis. The incumbent will be responsible for building, implementing, automating, and maintaining workflows and pipelines needed to bring AI Solutions to production focusing on scale and reliability.

What you will do

• Define, design, and build pipelines for deploying AI/ML applications to all major cloud platforms leveraging software engineering best practices and robust coding standards.
• Utilize Terraform, Helm charts, GitHub Actions, ArgoCD, and Pulumi to manage infrastructure as code and ensure seamless deployment processes.
• Ensure best practices are followed for development, integration, deployment, monitoring, and maintenance of applications across all major cloud providers (AWS, Azure, GCP).
• Collaborate with data scientists and machine learning engineers to integrate MLOps practices, ensuring smooth model deployment, monitoring, and lifecycle management.
• Develop and maintain CI/CD pipelines to automate the deployment process and improve efficiency.
• Troubleshoot and resolve issues related to deployment, infrastructure, and performance.
• Mentor engineers and promote a culture of continuous improvement and shared ownership.
• Ensure optimization for cost, scalability, and performance.
• Partner with stakeholders across the organization to provide technology selection recommendations and ensure that cloud architecture and deployment automation meets strategic business objectives.
• Implement best practices for system security, ensuring data integrity and compliance with industry standards.
• Ensure the solutions design of all deployments meet appropriate security and privacy regulations.
• Develop and maintain comprehensive documentation for AI Cloud architecture, design, and processes.
• Influence how applications are designed, built, and integrated on cloud platforms.

What we are looking for

• Bachelor’s degree or equivalent in Computer Science, Engineering, or a related field.
• 7 to 10 years of hands-on experience in DevOps/MLOps roles, with a strong focus on deploying and managing cloud-native applications on Kubernetes clusters at a high-tech, global company.
• Demonstrated experience in delivering cloud-based solutions with a strong focus on product development, including a proven ability to transition seamlessly from operational roles to product-focused responsibilities, ensuring the delivery and successful implementation of AI/ML applications.
• Proficiency in Terraform, Helm charts, GitHub Actions, ArgoCD, and Pulumi.
• Experience with cloud platforms (AWS, Azure, GCP) and their respective services.
• Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK stack).
• Strong understanding of CI/CD pipelines and best practices.
• Strong understanding of infrastructure as code principles and best practices.
• Knowledge of machine learning fundamentals and data processing frameworks.
• Familiarity with MLOps practices, including model deployment, monitoring, and lifecycle management.
• Excellent problem-solving skills and the ability to troubleshoot complex deployment issues.
• Strong written and verbal communication skills, with the ability to effectively collaborate with cross-functional teams.
• Agile and adaptable; excels at managing change and evolving technology.
• Proven ability to realign technical priorities rapidly to meet evolving business requirements.
• Proven track record implementing, standardizing, and automating scalable and reliable AI and ML solutions.
• Proven track record of successfully developing, integrating, deploying, and managing large-scale applications in a cloud environment.
• Experience in mentoring engineers.

Perks and Benefits

• Flexible vacation and Kinaxis Days (company-wide day off on the last Friday of every month).
• Flexible work options.
• Physical and mental well-being programs.
• Regularly scheduled virtual fitness classes.
• Mentorship programs and training and career development.
• Recognition programs and referral rewards.
• Hackathons.

For more information, visit the Kinaxis web site at www.kinaxis.com or the company’s blog at http://blog.kinaxis.com. Kinaxis strongly encourages diverse candidates to apply to our welcoming community. We strive to make our website and application process accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at recruitmentprograms@kinaxis.com. This contact information is for accessibility requests only and cannot be used to inquire about the status of applications.

Kinaxis is committed to ensuring a fair and transparent recruitment process. We use artificial intelligence (AI) tools in the initial step of the recruitment process to compare submitted resumes against the job description, to identify candidates whose education, experience and skills most closely match the requirements of the role. After the initial screening, all subsequent decisions regarding your application, including final selection, are made by our human recruitment team. AI does not make any final hiring decisions.