Kinterra is a private equity platform dedicated to building secure supply chains for the modern economy by acquiring and developing critical minerals and related infrastructure assets. With approximately US$1.5 billion in committed capital, Kinterra invests in high-quality, development-stage mining and downstream infrastructure projects to enable the energy transition, support infrastructure expansion, and advance global manufacturing resilience. We work alongside our portfolio companies to unlock value through active ownership, deep technical expertise, and disciplined project development. Our culture is rooted in collaboration, accountability, and excellence in execution. About the Role The AI Solution Architect will design, build, and operationalize reliable AI solutions across Kinterra and its portfolio companies. Reporting to Technology Innovation Officer, this hands‑on senior individual contributor will turn proven prototypes and emerging use cases into secure, reusable, measurable workflows that business and technical teams can depend on across the organization and its portfolio companies. Kinterra has already established a portfolio of working AI‑enabled reporting, knowledge, analytical, and operational workflows. These solutions reduce repetitive work, improve access to decision context, strengthen traceability, and accelerate business and technical analysis. The next step is to make the underlying architecture and AI‑enabled applications easier to scale, test, monitor, maintain, and support as adoption grows. Key Responsibilities: Architecture & standards. Define the target architecture and engineering standards for AI assistants, agents, retrieval systems, tools, and workflows; decide when to use direct model APIs, agent frameworks, vendor platforms, or custom orchestration; and maintain documentedpatterns, decisions, and playbooks that others can safely extend. Reusable platform components. Build common components for tool registration,



context and memory, orchestration, model routing, structured outputs, permissions, human approval, logging, and workflow execution so new solutions inherit a paved road. Pilot‑to‑production delivery. Take successful prototypes through the unglamorous middle of the delivery curve — hardening them for reliability, assigning ownership and service expectations, and driving them into sustained business use. Evaluation, reliability & observability. Create representative test sets, automated evaluations, regression controls, and release gates so behavioural changes are measured rather than judged informally; design for error handling, retries, timeouts, fallbacks, and rollback; and monitor quality, failures, usage, cost, data availability, and user outcomes. Business workflow integration & scale‑up. Partner with business and technical owners to translate ambiguous needs into scoped solution designs, then assess operational and commercial feasibility, test key uncertainties, and integrate the resulting solution into existing processes. Secure enterprise integration & delivery. Build maintainable APIs, services, data integrations, and user‑facing AI‑enabled applications, applying least privilege, secret management, data classification, privacy controls, and traceable actions. Cost & performance engineering. Measure and optimize model, token, compute, storage, and vendor consumption, and define fit‑for‑purpose model and reasoning choices that balance quality, latency, and cost. What you Bring Education. A Master's degree or PhD in Computer Science, Engineering, Machine Learning, Data Science, or a closely related STEM field. Production experience. Five or more years in software, data, automation, or machine‑learning engineering, including at least two years designing and building modern AI applications beyond basic API consumption, with evidence of taking at least one such application from prototype through real user adoption and sustained operation — including a explicit

📌 Ai Solutions Architect (Toronto)
🏢 Iris Software
📍 Toronto