AI Implementation Engineer (Azure AI, Agents & Automation)

About Tribe

Tribe Property Technologies is modernizing one of Canada’s most traditional industries — property management — through technology and community-driven innovation. Our platform, Tribe Home, connects residents, managers, and service providers in one ecosystem.

We’re now taking the next leap: embedding AI and automation across Tribe as a whole (internal operations and customer-facing experiences) while keeping security, reliability, and Canadian data residency front and center.

The Role

We’re looking for a hands-on AI Implementation Engineer to help us design, improve, and deploy production-grade AI systems and automations across Tribe. This role spans both internal automation and customer-facing AI:

• Internal: high-impact workflow automation that reduces friction for staff
• Customer-facing: AI capabilities embedded into Tribe Home that improve the community management experience and resident service

Work in a modern Azure-based environment (Azure SQL, .NET, React, SQL Server, MongoDB).

Build solutions using:

• Azure AI Foundry / Azure OpenAI
• Copilot Studio + Power Automate
• Microsoft Fabric (Lakehouse and data readiness)

Leverage MCP (Model Context Protocol) to integrate and orchestrate AI capabilities across systems.

What Success Looks Like

In the first 90 days

• Review our existing AI implementations in Azure and identify architecture + reliability improvements
• Implement key improvements (patterns, guardrails, observability, integration approach)
• Deliver a clear strategy to support the next 12–24 months of goals
• Ship one high-value internal automation that meaningfully reduces time and friction for staff

In 12 months (home run)

• Be a key designer and implementer of Tribe’s AI systems (internal + Tribe Home)
• Establish reusable implementation patterns (including MCP tool patterns) the team can follow
• Deliver multiple meaningful automations and AI features with measurable outcomes
• Raise the bar on AI reliability (testing, evaluation, monitoring) across what we build

What You’ll Do

• Evaluate and improve existing AI architecture and implementations in Azure (reliability, scalability, security, cost)
• Build RAG (retrieval-augmented generation) and agentic workflows that safely take actions with appropriate guardrails
• Design and implement AI orchestration using Azure AI Foundry / Azure OpenAI, and integrate with Power Automate / Copilot Studio where it makes sense
• Extend and operationalize our MCP server as a tools abstraction layer:
  • design MCP tools over APIs/legacy systems
  • enforce permissions, auditability, and safe action patterns
• Work with Product and Engineering to deliver customer-facing AI in Tribe Home (e.g., ticket triage/summarization, knowledge retrieval over documents)
• Identify and implement high-value internal automations (approvals, routing, document-triggered workflows, reporting)
• Contribute to data readiness work (Fabric/Lakehouse exposure, data quality expectations, AI-ready datasets)
• Implement AI reliability practices: prompt/context patterns, evaluation, human-in-the-loop design, monitoring/telemetry
• Collaborate with IT, Operations, and Service Delivery as secondary partners to ensure solutions work in real workflows

Mentoring is valuable but secondary; we primarily need a strong builder who can also guide patterns by example.

What You Bring

Required

• 4+ years in software engineering, automation engineering, or similar (strong “builder” track record)
• Experience with .NET and C# in production environments
• Proven experience implementing AI systems using Azure OpenAI / Azure AI services (or equivalent production LLM systems)
• Strong integration skills: APIs, authentication, data access patterns, event-driven workflows
• Experience building RAG systems and/or agentic workflows with clear reliability and safety patterns
• Comfort writing code to solve orchestration and integration problems (not low-code only)
• Experience with Power Automate and/or Copilot Studio (or ability to ramp quickly)
• Practical knowledge of AI pitfalls: hallucinations, prompt brittleness, tool misuse, data quality dependency, governance

Nice to Have

• Microsoft Fabric / Lakehouse exposure; basic data modeling and analytics awareness
• Experience with SQL Server, MongoDB, Azure DevOps, Graph Databases
• Experience with AI evaluation/observability tools and practices
• Familiarity with vector search / knowledge retrieval patterns (e.g., Azure AI Search)
• Data science experience (helpful, not required)

Why You’ll Love Workin