Senior Ai Architect

2 weeks ago

Mississauga, Ontario, Canada Fuze HR Solutions Full time

We are seeking an experienced AI Architect to join a dynamic team. This role involves designing, integrating, leading, and maintaining the end-to-end architecture of a portfolio of Advanced Analytics (AA) and Artificial Intelligence (AI) platforms and frameworks. The ideal candidate will be a visionary leader who can set the direction for AI and Generative AI (GenAI), collaborating with key stakeholders and working closely with architects, product managers, and technology leaders to drive AI adoption across the organization.

Roles and Responsibilities:

  • Technology Vision & Strategy:
    Define the AI and GenAI technology roadmap, ensuring alignment with business goals and IT strategy. Identify emerging AI trends and assess their impact on the organization. Contribute to strategic planning by establishing a technology roadmap that aligns business and IT strategies, enabling a portfolio to support both current and future business capabilities.
  • Enterprise AI Architecture:
    Establish architectural principles, guidelines, and best practices for AI and GenAI solutions, focusing on security, scalability, and performance. Design end-to-end solutions based on cloud components for AA & AI, including tracking, KPI reporting, and adhering to architecture principles and guidelines. Promote reusable technology to maintain an architectural runway leveraging composable architecture.
  • Stakeholder Collaboration:
    Work closely with business leaders, product managers, and technology teams to identify AI-driven opportunities and define value-driven use cases. Promote the reuse of AA & AI platforms and frameworks across the enterprise.
  • Cross-functional Leadership:
    Collaborate with enterprise and solution architects, data teams, and engineering leads to integrate AI capabilities into existing and new systems.
  • AI Governance & Ethics:
    Develop and enforce AI governance frameworks, ensuring responsible AI adoption, compliance, bias mitigation, and ethical considerations.
  • Technology Evaluation & Prototyping:
    Assess, recommend, and experiment with AI/ML tools, frameworks, and platforms to accelerate AI adoption and innovation. Drive the design, engineering, and reuse of applications or patterns, creating Enabler Epics for solution architectures to ensure continuous value flow and faster time-to-market.
  • Scalability, Quality & Performance:
    Ensure AI solutions are architected for enterprise-scale deployment, balancing cost, performance, security, and operational efficiency. Ensure each solution component is designed to run as expected, with built-in observability and a system-wide view across the full value stream. Drive continuous product quality improvement by applying Quality Attributes (NFRs) at relevant levels (portfolio, product lines, products).
  • Coaching & Thought Leadership:
    Mentor teams on AI/GenAI best practices, contribute to industry forums, and represent the organization's AI strategy in key discussions.

Qualifications and Experience:

  • Expert experience in architecting both AWS-based and Azure-based cloud solutions.
  • In-depth understanding of AI, GenAI, LLMs, MLOps, vector databases, and Advanced Analytics.
  • Experience with agentic AI and agent orchestration.
  • Strong knowledge and hands-on experience with RAG (Retrieval-Augmented Generation).
  • A hands-on attitude with the ability to explore new technologies and remain an active developer.
  • Experience managing AI at scale, including optimizing cost through token management, model selection, cloud, and on-prem compute.
  • Proven experience in developing architecture roadmaps for a portfolio of products in collaboration with enterprise architecture teams.
  • Strong knowledge of AI governance, model lifecycle management, security, and ethical AI principles.
  • Hands-on experience in AI solution development, including data pipelines, model training, fine-tuning, and AI integration into enterprise applications.
  • Experience in cloud-native architectures and distributed AI model deployment.
  • Excellent ability to engage with and communicate AI concepts effectively to both technical and non-technical stakeholders.
  • Proven track record of leading AI transformation initiatives in large organizations.

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