Head of Frontier Technologies

Job Responsibilities

• Define the firm's point of view on AI/ML and emerging technology adoption across a diverse portfolio (data center operations, land/real estate strategy, construction, legal ops, EHS, CRM/quote-to-cash, and adjacent domains).
• Build a prioritized, portfolio-wide AI/ML Strategy and roadmap that balances quick wins against multi-year platform bets.
• Evaluate build-vs-buy-vs-partner decisions for AI/ML capabilities, including LLM-based systems, data platforms, and vertical SaaS with embedded AI.
• Track the frontier: new models, agent architectures, and tooling — translate what's real and applicable versus hype, and update the roadmap accordingly.
• Set reference architectures and standards for AI/ML systems used across portfolio companies (e.g., LLM-centric applications with vector-backed data stores, integration patterns with core systems of record such as NetSuite, Coupa, and industry-specific platforms).
• Establish data architecture principles — source-of-truth design, pipeline standards, and governance — that let AI initiatives scale across companies without re-solving the same problems.
• Define evaluation frameworks for new platforms (EHS systems, DCIM/monitoring tools, construction tech, legal spend management) with AI/ML capability as an explicit selection criterion.
• Personally lead (or embed with) delivery teams to design and ship priority AI/ML initiatives from concept through production.
• Stand up or oversee integrations between AI systems and enterprise platforms already in use across the portfolio.
• Own program management for cross-portfolio technology initiatives: scoping, sequencing, resourcing, and risk management.
• Define and track KPIs that tie technology execution to business outcomes (cycle time, cost avoidance, revenue impact, risk reduction).

Portfolio Company Engagement

• Act as the trusted technology partner to portfolio company CEOs/COOs — translating firm-wide strategy into a plan that fits each company's stage, resources, and risk tolerance.
• Run technology and AI readiness assessments for new and existing portfolio companies, including as part of diligence for acquisitions.
• Build internal capability at the portfolio company level so initiatives are sustainable without permanent central-team dependency.

Governance, Risk & Vendor Management

• Establish AI governance practices: data privacy, model risk, compliance (including any sector-specific regulatory considerations), and responsible-use policies.
• Own the vendor landscape for AI/ML and adjacent platforms — evaluation, negotiation support, and lifecycle management.
• Partner with legal/compliance on emerging regulatory and IP considerations tied to AI adoption.