VP, AI Product Manager - Data & AI

About this role

Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee-focused programming, we are committed to fostering a welcoming and inclusive work environment where high-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry.

Job Description The VP, AI Product Manager is a senior product leader within the central hub, responsible for defining what the firm builds with AI and why — translating the needs of investment professionals, operations teams, IR, legal, and compliance into a coherent AI use-case portfolio that delivers measurable business value. This role owns the AI use-case roadmap, the intake-to-delivery lifecycle, and the AI governance gate process that moves use cases from concept through Legal, Compliance, Risk, and Cyber sign-off into production. The VP serves as the connective tissue between business stakeholders who surface problems, the vertical technology teams, and AI Engineering hub that builds the platform to solve them. This is a strategic and execution-oriented role in equal measure. You will partner with vertical PM tech teams and run discovery with deal teams, IR, and operations; write crisp product specs that engineering can execute against; define success metrics; and manage the use-case funnel across investment and corporate functions. The AI Product Management function owns the demand side of the AI platform: what gets built, in what order, and with what definition of success. Vertical spoke teams in each investment vertical surface use-case demand; the VP, AI Product Management qualifies, prioritizes, and shepherds the most valuable use cases through delivery as centrally-built platform capabilities, applying the two-vertical rule as the primary governance lens for hub vs. spoke build decisions. Key Responsibilities AI Use-Case Strategy & Roadmap • Own the enterprise AI use-case roadmap across all firm functions — deal execution, portfolio operations, investor relations, legal & compliance, and firm-wide productivity — with prioritization aligned to business leadership • Apply the two-vertical rule as the governing framework: when a use case is applicable across two or more verticals or functions, lead the business case for centralizing on the hub platform rather than rebuilding in each spoke • Define and maintain the AI use-case intake funnel: structured discovery, feasibility scoring, value estimation, and sequencing logic that balances strategic impact with engineering capacity • Translate high-level business goals (analyst time savings, decision support, operational efficiency) into a portfolio of AI initiatives with clear owners, milestones, and success criteria • Stay current on generative AI and agentic capabilities; proactively identify where emerging platform primitives (MCP integrations, A2A workflows, new model capabilities) unlock net-new use cases for the firm

Discovery, Scoping & Requirements • Lead structured discovery sessions with deal teams, portfolio operations, IR, legal, compliance, and senior stakeholders to surface high-value AI opportunities and translate them into product requirements • Produce well-structured product specifications: user stories, workflow diagrams, acceptance criteria, context layer definitions (Firm/Deal/User), retrieval scope, and output format requirements — written to the standard AI Engineering executes against • Distinguish between use cases suited for RAG-based retrieval, agentic orchestration, structured extraction, or analytical AI — and articulate the distinction clearly to both technical and business audiences • Own MNPI sensitivity classification for each use case; partner with Data Governance, Legal and Compliance during scoping to determine information barrier requirements before engineering engagement • For portfolio operations and reporting use cases, collaborate with Data Product Management to ensure Gold-layer data products required for AI retrieval are defined and on roadmap before engineering begins

AI Governance Gate & Compliance • Own the AI governance gate process end-to-end: producing the use-case submission package for Legal, Compliance, Risk, and Cyber sign-off and driving each use case through the gate to approved status • Support the governance submission template and ensure all AI use cases — regardless of vertical or function — follow a consistent review process before production deployment • Manage ongoing governance obligations post-deployment: monitoring thresholds, model change notifications, and periodic reviews required by compliance stakeholders • Partner with Data Governance on AI-specific policy: retrieval permissioning, audit log requirements, acceptable use definitions, and model output disclaimers • Serve as the business-side point of contact for ARB (Architecture Review Board) submissions on AI use cases, coordinating with AI Engineering on technical evidence packages

Delivery Partnership & Use-Case Lifecycle • Partner with the Principal AI Engineer and AI Engineering team through the full use-case lifecycle: from approved spec through build, evaluation, staged rollout, and production launch • Define evaluation criteria and user acceptance tests for each use case — retrieval quality benchmarks, output format adherence, latency thresholds, and qualitative review with business stakeholders • Own the feedback loop post-launch: structured user feedback collection, monitoring of observability metrics, and iteration prioritization with AI Engineering • Track time-to-value for each use case from intake to production; report use-case portfolio status and business impact to business leadership on a regular cadence • Manage the transition

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