Director of AI

• 5+ years in a senior leadership role designing and deploying AI systems at scale
• Proven, hands-on experience building ML models in Databricks (not just managing teams that do)
• Deep experience with PySpark/Spark ML, modern Python ML frameworks, and end‑to‑end ML lifecycle management, from model training through deployment and ongoing monitoring
• Experience integrating ML/AI solutions into production systems
• Experience translating business problems into technical solutions and back again
• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related technical field required, or equivalent practical experience

Design, build, and maintain end-to-end AI solutions using Databricks, including:

• Feature engineering pipelines
• ML model training, evaluation, and tuning
• Model deployment and monitoring in production
• Develop machine learning models directly in Databricks (e.g., predictive models, classification, forecasting, NLP, and applied generative AI use cases)
• Leverage Databricks AI capabilities (MLflow model registry, feature stores, notebooks, jobs, and orchestration)
• Build and integrate AI solutions with existing enterprise systems (Yardi, Salesforce, ERP, BI platforms, internal applications)
• Prototype quickly, validate value, and harden solutions for production use

AI Platforms & Tooling

• Design and implement AI solutions using:
• Databricks (core requirement)
• Microsoft Copilot Studio and related Microsoft AI services
• Azure-native data and AI services where appropriate
• Evaluate when to use traditional ML vs. generative AI vs. automation
• Own the technical decisions around model selection, architecture, and scalability

AI Strategy

• Define and maintain the enterprise AI roadmap based on what can be realistically built and supported
• Identify high-value, practical AI use cases tied to measurable business outcomes (cost reduction, revenue lift, operational efficiency)
• Balance experimentation with production readiness

Governance, Reliability & Responsible AI

• Implement practical AI governance that does not slow delivery, including:
• Model versioning and lineage
• Performance monitoring and drift detection
• Data privacy and security controls
• Ensure AI systems are explainable, auditable, and aligned with internal standards

“Our specialized recruiting professionals apply their expertise and utilize our proprietary AI to find you great job matches faster.”