Keyrus is an international group of 3,000 experts across 30 countries, built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have helped organizations build the data foundations that power intelligent systems. Today, we go further by designing and operationalizing intelligence that delivers measurable business value. Building AI capabilities is only part of the challenge. Real transformation happens when those capabilities work within existing business systems, data environments, workflows, and processes. At Keyrus, you'll work closely with clients to design the architecture and technical foundations that turn AI opportunities into secure, scalable, production‑ready solutions. Technology amplifies. Industrial discipline connects the two.

Location:
Hybrid
- Montreal or Toronto, Canada (2 days per week in the office) Contract: Permanent, full-time What You'll ArchitectAs an AI Foundation Architect, you'll design the technical foundations that enable organizations to develop, deploy, and scale AI and agentic solutions in enterprise environments. You'll connect cloud infrastructure, data platforms, enterprise applications, models, agents, and security capabilities to create reliable architectures that support business needs. Your focus goes beyond solution design. You'll define architecture principles, make key technical decisions, and guide engineering teams in building secure, scalable, and cost‑effective AI systems. Lead architecture discovery with clients to understand business objectives, existing systems, technical constraints, and AI requirements. Define target architectures for production AI and GenAI solutions, integrating models, agents, cloud services, enterprise data, applications, and APIs.



Own key architecture decisions within the assigned scope, establishing reference patterns, technical boundaries, and design standards. Design secure and reliable foundations addressing identity, access, scalability, resilience, observability, and operational requirements. Evaluate architecture trade‑offs, including model selection, performance, latency, infrastructure costs, and AI consumption patterns. Guide AI Engineers, Data Engineers, Software Engineers, and cloud specialists to ensure consistency across technical implementations. Advise client stakeholders, review technical designs, and document reusable architecture patterns and decisions. Convert successful architecture decisions into reusable Keyrus assets, including reference architectures, decision records, standards, patterns, and design guidance that can strengthen future engagements. Who You AreYou are an experienced technical architect who combines strategic architecture judgment with recent practical understanding of production AI systems. You understand how cloud, data, applications, and AI technologies work together, and you can translate business requirements into practical architecture decisions. You are comfortable evaluating technical trade‑offs, guiding engineering teams, and advising clients while respecting specialist ownership. You combine strong technical judgment with transparent communication, problem‑solving, and leadership skills.



Strong experience, 8 years +, designing and owning complex enterprise, solution, cloud, or data architectures in production environments. Experience defining reference architectures, technical standards, integration patterns, and architecture decisions. Experience designing distributed systems and enterprise integrations across multiple technical environments. Ability to evaluate architecture trade‑offs involving security, reliability, scalability, performance, maintainability, and cost. Experience guiding multidisciplinary engineering teams and advising client architects, technical leaders, and business stakeholders. Technical Skills Deep architecture expertise in at least one major cloud ecosystem, such as AWS, Azure, or GCP, including relevant AI services. Experience designing architecture for model access and deployment patterns across managed AI services, APIs, open‑source models, or comparable enterprise AI environments. Strong understanding of GenAI architectures, including LLMs, RAG, AI agents, model integration, and enterprise AI workflows. Knowledge of enterprise data architectures, including lakehouse, streaming, data mesh, or feature stores. Understanding of security architecture, IAM, networking, resilience, observability, and production operations. Familiarity with MLOps, LLMOps, CI/CD, containers, infrastructure as code, and AI cost optimization. Experience with Amazon Bedrock, Azure AI, Vertex AI, or comparable cloud AI services. Experience designing multi‑agent systems, model routing, or AI orchestration architectures. Familiarity with LangGraph, MCP, AI gateways, or similar technologies. Experience with regulated environments, AI governance, or formal architectu

📌 Ai Foundation Architect (Winnipeg)
🏢 Keyrus
📍 Winnipeg