AI Architect to design and build an enterprise-grade, AI-enhanced software engineering platform that improves developer productivity, code quality, and engineering effectiveness. hands‑on architecture and platform engineering role focused on integrating enterprise AI capabilities into software development workflows. The ideal candidate combines strong enterprise architecture skills with practical experience building secure, scalable, governed AI platforms using managed LLM services. Enterprise AI Architecture & Platform Engineering – Designing and productionizing secure, reusable AI platforms using managed foundation‑model services. Governed model access, AI gateways, model routing, authentication, usage controls, and cost monitoring. Retrieval‑augmented generation, embeddings, chunking, hybrid search, retrieval evaluation, Azure AI Search, and PostgreSQL with pgvector. Azure DevOps, GitLab, GitHub, Jira, Confluence, REST APIs, webhooks, CI/CD, and software development workflows. Cloud Security & AI Governance – Microsoft Entra ID, managed identities, Azure Key Vault, least‑privilege access, prompt‑injection defence, privacy, and auditability. OpenTelemetry, Azure Application Insights, telemetry, performance monitoring, reliability, and cost optimization. Enterprise Knowledge Architecture – target‑state AI architecture , implementation roadmap, and prioritized MVPs based on business value, risk, and delivery effort. Integrate AI capabilities into developer workflows, initially leveraging GitHub Copilot and Azure DevOps repositories, work items, pipelines, and service hooks.



Establish enterprise architecture standards, security controls, reference implementations, design documentation, and operational runbooks. Collaborate with Information Security, Cloud Engineering, Privacy, Risk, Architecture Review Board, and AI governance stakeholders. Partner with engineering teams through hands‑on implementation, technical guidance, design reviews, and knowledge transfer. Define evaluation and monitoring practices to measure AI quality, developer productivity, reliability, security, and cost effectiveness. enterprise AI architecture, AI platform engineering, and production LLM integration . Hands‑on experience with managed LLM services and enterprise AI governance patterns. RAG, vector retrieval, context engineering, and AI workflow orchestration . Azure cloud security, identity management, data protection, and observability . Experience with contemporary software engineering toolchains, CI/CD, source control, and application security practices. Ability to translate enterprise requirements into secure, scalable architectures and deliver pragmatic, production‑ready MVPs. Excellent communication and stakeholder management skills, with the ability to influence architecture decisions and mentor engineering teams. This role offers the opportunity to shape an enterprise AI platform from architecture through implementation, enabling responsible AI adoption across software engineering teams. building practical AI capabilities, not training AI models
- connecting approved LLMs, enterprise knowledge, and engineering tools through secure, reusable, and measurable platform services. #

📌 Architecte Online (Winnipeg)
🏢 Coforge
📍 Winnipeg