Director of Artificial Intelligence

AI ARCHITECT & ENGINEERING DIRECTOR

Overview

Hands-on technical leader responsible for designing, building, and scaling AI solutions across the business. This role combines deep Databricks expertise, modern AI platforms, and Microsoft’s AI ecosystem with the ability to turn real business problems into production-ready systems.

You won’t just set strategy—you’ll build. This person operates as both the lead AI engineer and the driver of enterprise AI adoption across operations, development, leasing, and asset management.

Responsibilities

Build & Deliver AI Solutions

• Design and deploy end-to-end ML solutions in Databricks (feature engineering, training, tuning, deployment, monitoring)
• Develop models across predictive analytics, forecasting, NLP, and generative AI
• Rapidly prototype, validate impact, and productionize solutions
• Integrate AI into enterprise systems (Yardi, Salesforce, ERP, BI, internal tools)

Own AI Platforms & Architecture

• Lead development using Databricks (core), Microsoft Copilot Studio, and Azure AI services
• Determine when to use ML, generative AI, or automation
• Make key decisions on model selection, architecture, and scalability

Drive AI Strategy

• Define and evolve a practical, execution-focused AI roadmap
• Identify high-impact use cases tied to cost savings, revenue growth, and efficiency
• Balance experimentation with production readiness

Governance & Reliability

• Implement scalable AI governance (model versioning, monitoring, drift detection, security)
• Ensure solutions are explainable, auditable, and compliant

Lead Execution

• Deliver AI products, tools, and automations end-to-end
• Manage vendors and external partners
• Build and lead a high-performing AI team
• Establish best practices, documentation, and adoption frameworks

Qualifications

• 5+ years leading and delivering AI/ML solutions at scale
• Hands-on experience building ML models in Databricks
• Strong expertise in PySpark/Spark ML, Python ML frameworks, and full ML lifecycle
• Proven experience deploying AI into production environments
• Ability to translate business needs into technical solutions
• Bachelor’s degree in a technical field or equivalent experience

Nice to Have

• Microsoft Copilot Studio / Microsoft AI ecosystem experience
• Background in real estate, finance, or asset-heavy industries
• Experience with MLOps and CI/CD for data and ML