VP, AI Product Manager - Data & AI

Ares Management Corporation•New York, NY

1d•$225,000 - $275,000

About The Position

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.

Requirements

8+ years in product management; 3+ years in an AI, ML, or data product role with direct ownership of LLM or generative AI use cases from discovery through production
Demonstrated experience translating complex, knowledge-intensive workflows (legal review, research, financial analysis, report generation) into AI product requirements that engineering teams can execute against
Track record of managing multi-stakeholder use-case portfolios with competing priorities across business functions — not just a single product line
Experience operating in or closely with regulated industries (financial services, legal, healthcare): familiar with governance gate processes, compliance review requirements, and the discipline of shipping AI responsibly
Strong understanding of RAG architectures, agentic workflows, prompt design tradeoffs, and retrieval quality concepts — sufficient to write precise requirements and hold technical conversations with AI Engineering
Exceptional written communication: able to produce crisp product specs, executive memos, board-ready summaries, and governance submission packages with equal fluency
Strong analytical reasoning: able to build business cases, quantify expected value (time savings, risk reduction, revenue support), and defend prioritization decisions with data
Highly organized with strong program management instincts — able to run a multi-use-case intake funnel, track parallel workstreams, and keep stakeholders aligned without losing velocity
Comfortable operating at both the strategic level (roadmap, prioritization, executive alignment) and the tactical level (writing acceptance criteria, reviewing retrieval results, coordinating rollout)
High accountability: takes ownership of outcomes, communicates blockers early, and builds trust with both technical and business partners

Nice To Haves

Private equity, investment banking, asset management experience — familiarity with deal workflows, IC memo process, fund reporting cycles, LP communications, or portfolio monitoring
Experience managing AI governance or responsible AI review processes in a regulated environment, including interactions with Legal, Compliance, or Risk functions
Familiarity with Databricks, Unity Catalog, or enterprise data platforms — sufficient to understand how data product maturity constrains AI use-case delivery
Experience with LLM evaluation frameworks (e.g., RAGAS, LLM-as-judge, human evaluation rubrics) and the ability to define measurable acceptance criteria for generative AI outputs
Prior experience in a hub-and-spoke or federated operating model — comfortable defining central platform policy while enabling vertical teams to move with autonomy
MBA or advanced degree in a relevant field (finance, computer science, information systems) is a plus but not required

Responsibilities

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
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
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
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 of use cases from spoke-built prototypes to hub platform capabilities, coordinating change management with vertical teams and their stakeholders
Build and maintain trusted relationships with senior stakeholders across investment verticals —deal professionals, operations, IR, and Legal and Compliance — as the face of the AI product function
Run structured demos, pilots, and feedback sessions with business users; translate qualitative feedback into actionable product decisions and communicate decisions back to stakeholders with clear rationale
Develop and execute change management plans for AI use-case launches, including user training, adoption tracking, and escalation pathways for issues
Partner with the Data & AI PMO on cross-functional coordination, sprint ceremonies, and executive reporting across the AI use-case portfolio
Champion responsible AI use within the firm: communicating model limitations, output uncertainty, and appropriate human-in-the-loop requirements to business users