Job Type: Permanent
Work Model: Hybrid
Reference code: 126891
Primary Location: Toronto, ON
All Available Locations: Toronto, ON; Brossard, QC; Burlington, ON; Calgary, AB; Edmonton, AB; Fredericton, NB; Halifax, NS; Kitchener, ON; Laval, QC; Moncton, NB; Montreal, QC; Ottawa, ON; Quebec City, QC; Regina, SK; Saint John, NB; Saskatoon, SK; St. John's, NL; Vancouver, BC; Victoria, BC; Winnipeg, MB
Our Purpose
At Deloitte, we are driven to inspire and help our people, organization, communities, and country to thrive. Our Purpose is to build a better future by accelerating and expanding access to knowledge. Purpose defines who we are and gives us reason to exist as an organization.
By living our Purpose, we will make an impact that matters.
• Have many careers in one Firm..
• Be expected to share your ideas and to make them a reality.
• Be part of a firm that leads the way and pushes themselves to look like contemporary Canada.
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About the team
We are seeking a highly experienced and innovative AI Solutions Architect with a strong foundation in traditional solution architect, full-stack development and cloud technologies with solid exposure and experience integrating enterprise platforms. In this role, you will be responsible for designing and implementing end-to-end AI-driven solutions while ensuring seamless integration with enterprise systems and cloud platforms. This position requires expertise in both traditional IT systems and cutting-edge AI/ML technologies, with a focus on building scalable, secure, and efficient architectures that leverage cloud-native services.
The ideal candidate will have deep knowledge of AI/ML models, modern development frameworks, and cloud services (particularly Azure), as well as experience integrating these with traditional enterprise platforms and infrastructures.
What will your typical day look like?
Key Responsibilities:
• AI Solutions Design: Lead the design, development, and deployment of AI and machine learning solutions, integrating them with existing enterprise systems or building cloud-native architectures.
• Enterprise Architecture: Architect traditional, cloud, and hybrid solutions, incorporating both on-premise and cloud-based systems (Azure) that support AI and full-stack applications.
• Full-Stack Development Leadership: Oversee full-stack development projects, guiding the design and implementation of both front-end (React, Angular) and back-end (Node.js, .NET) components, while integrating AI-powered features.
• Cloud Integration (Azure): Architect and implement solutions using Microsoft Azure services (e.g., Azure Machine Learning, Azure Kubernetes Service, Azure App Services, Cosmos DB) to develop, deploy, and scale AI/ML models and applications.
• AI/ML Pipelines: Design and implement AI model development pipelines, including data ingestion, model training, and deployment, while ensuring integration with enterprise data sources.
• Traditional and Modern Systems Integration: Architect solutions that seamlessly integrate traditional enterprise systems (e.g., CRM, ERP, data warehouses) with AI/ML models and modern full-stack applications.
• Security and Compliance: Ensure that AI solutions and full-stack architectures comply with security best practices, data governance, and industry-specific regulations, both in on-premise systems and cloud environments.
• Data Engineering: Design data architectures and pipelines to support AI and ML initiatives, ensuring high availability, performance, and scalability.
• Technical Leadership: Provide technical leadership across AI, cloud, and full-stack development teams, guiding best practices, architecture reviews, and strategic decisions.
• DevOps Integration: Implement DevOps practices (CI/CD, infrastructure as code) for AI model deployment and full-stack application development using tools like Azure DevOps, Docker, Kubernetes, and Terraform.
• Collaboration and Stakeholder Engagement: Work closely with business stakeholders, developers, and data scientists to translate business needs into scalable technical solutions.
• Documentation and Governance: Develop architecture documentation, technical specifications, and maintain governance over AI and cloud solutions to ensure alignment with business objectives and technical standards.
Enough about us, let’s talk about you
Required Skills and Qualifications:
• Education: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• Experience: 8+ years of experience in solutions architecture, with at least 3 years focused on AI/ML solutions and a background in full-stack development.
• AI/ML Expertise: Strong understanding of machine learning models, deep learning, natural language processing (NLP), computer vision, and AI tools (e.g., TensorFlow, PyTorch).
• Azure Cloud Expertise: Proven experience with Microsoft Azure services (e.g., Azure App Services, Functions, AKS, Azure Machine Learning, Azure DevOps) and deployin
Location: Toronto, ON
Posted: Oct. 31, 2024, 3:35 a.m.
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