Senior AI Platform Engineer

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Charlotte, NC, US

8 days ago Requisition

Quarterra is a real estate investment firm focused on creating long-term value through the development of high-quality multifamily communities nationwide. With 11 regional offices across 20 states, Quarterra combines institutional scale with local market expertise to deliver purposefully designed rental communities in high-growth markets. Quarterra's core strategy includes the expansion of its Emblem portfolio - a growing collection of attain ably priced communities that deliver efficient design, modern amenities, and strong investment fundamentals.

Emblem Communities are thoughtfully positioned to meet the needs of today's renters while supporting Quarterra's broader vision of sustainable, resilient, and diversified housing solutions.

Reporting to the Senior Director, the Senior AI Platform Engineer builds, deploys, secures, monitors, and maintains Quarterra's enterprise AI and automation environments. This senior individual contributor owns production systems end to end and is accountable for platform reliability, quality, security, and operational performance.

The role advances Quarterra's Agentic Workspace and Unified Data Strategy by engineering AI agents, enterprise automations, integrations, and the business context layer that grounds AI outputs. This is an AI engineering and platform ownership role, not a reporting, analytics, or business analysis position. The engineer partners with internal stakeholders, technology team members, and software vendors to move AI capabilities from pilots into reliable production workflows.

This position is preferably based in Charlotte, NC and follows a hybrid work schedule at our headquarters. Quarterra is also open to considering highly qualified remote candidates within the Eastern or Central time zones who can effectively support collaboration across the organization with travel to Charlotte Headquarters, as needed.

• Agent Engineering and Orchestration
  • Design, build, test, deploy, and maintain AI agents end to end, including retrieval design, context assembly, tool and function definitions, orchestration logic, and structured handoffs when human judgment is required.
  • Engineer the business context layer that grounds agents, including source connectors, indexing strategy, metadata, permission propagation, data freshness, and reconciliation.
  • Build evaluation harnesses using test datasets, accuracy and grounding metrics, measured baselines, and regression suites to detect quality drift before and after deployment.
  • Design guardrails and failure behavior, including confidence thresholds, refusal paths, human review checkpoints, and safe fallback processes when an agent is operating outside reliable boundaries.
  • Train users on Quarterra AI capabilities and Microsoft Copilot usage, and facilitate recurring learning sessions that support responsible adoption across business functions.
• Agentic Workspace and Enterprise Automation
  • Automate high-volume, rules-based processes such as document intake and extraction, routing, approvals, and data reconciliation using code, APIs, Model Context Protocols (MCPs), and event-driven services.
  • Build Microsoft 365 automation, including Microsoft Copilot and Copilot Studio extensibility, Microsoft Graph API integrations, SharePoint and Teams solutions, and Power Platform solutions where appropriate, with custom services where required.
  • Replace manual handoffs with optimized, observable, and idempotent pipelines that can run safely without duplicating work.
• Platform and Integration Ownership
  • Serve as Quarterra’s accountable technical owner for AI and automation platforms, including environments, configuration, releases, upgrades, regression testing, and production support
  • Own integrations between Microsoft- and Amazon Web Services-based platforms and Quarterra systems, including APIs, authentication, data contracts, and schema versioning in support of the Agentic Workspace and Unified Data Strategy.
  • Manage technical support relationships with software vendors, including defect reproduction, escalation, roadmap input, and validation of vendor-delivered work against documented acceptance criteria.
• Reliability, Security, and Operations
  • Establish and own service-level key performance indicators for production automations and AI agents, build monitoring that measures performance and accuracy, and lead response and remediation when targets are not met.
  • Implement least-privilege access for agents and service accounts using enterprise identity tools, ensuring agents follow the permissions of the users they serve and do not access unauthorized data.
  • Maintain technical controls supporting responsible AI operations, including audit logging, data lineage, prompt and model version control, and…