About Us 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 documented patterns, 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 clear account of failures encountered and how the design changed.

Software & DevOps engineering. Strong software and API engineering skills, including Python, modular design, versioning, testing, dependency management, and maintainable repository practices, along with cloud deployment, CI/CD, secrets management, and practical incident troubleshooting.

Applied ML, AI systems & evaluation. Solid understanding of machine-learning and contemporary AI application patterns — model selection, retrieval and context design, reliable structured outputs, workflow orchestration, and human review — plus the ability to design representative test cases, quality metrics,



regression controls, telemetry, and operational dashboards appropriate to probabilistic systems.

Investment & sector operating context. Exposure to private equity, asset management, portfolio-company operations, financial analysis, or diligence — or comparable experience in a financial-services, engineering, mining, energy, or infrastructure setting — where source accuracy, commercial judgment, and auditability materially affect decisions.

Business & cross-functional partnership. Ability to work directly with non-technical leaders, challenge unclear requirements constructively, explain trade-offs plainly, and align business owners, domain experts, engineers, vendors, and control functions without relying on formal authority.

Scale-up & practical problem-solving. A hands-on, hypothesis-driven approach to troubleshooting complex systems — separating root causes from symptoms and resolving the highest-risk constraint first — and the ability to move an innovative concept toward dependable operation by testing assumptions and adjusting the development path as evidence changes.

Enterprise integration.

Experience with Microsoft 365, SharePoint, Microsoft Graph, Azure, identity platforms, business applications, or comparable enterprise data and deployment environments.

Vendor, platform & partner judgment.

Experience evaluating and integrating leading model providers, enterprise AI platforms, specialized research tools, and agent frameworks without becoming locked into one implementation pattern, and identifying where vendors, startups, research institutions, or technical partners can accelerate delivery while retaining clear ownership of architecture and outcomes.

Specialized application integration.

Experience integrating document-intensive, analytical, engineering, financial, or desktop software through APIs, controlled automation, or human-reviewed computer use.

Enterprise security. Working knowledge of identity, authorization, secrets, data protection, audit trails, and secure integration patterns for confidential business information. This posting reflects a new current vacancy and Kinterra’s commitment to a fair and transparent recruitment process.

We use AI enabled tools to support parts of the hiring process, however they are guided closely by humans. All interviewed candidates will be notified of the hiring outcome within 45 days, and accommodations are available upon request.

📌 AI Solutions Architect (Toronto)
🏢 Kinterra Professional Services Group
📍 Toronto