Job SummaryThe Product Manager, AI is responsible for driving the development and delivery of AI capabilities across Allvue's fund accounting, investment operations, and investor relations products for private equity clients. Working closely with engineering, clients, and cross-functional partners, this role translates market, client, and business needs into product requirements and priorities that improve the platform and support client workflows. The Product Manager brings strong private equity fund accounting and investment operations expertise, pairs it with hands-on experience putting AI into production, manages product execution within an assigned area, and serves as a key connection between clients and the teams responsible for building and delivering the product.
ResponsibilitiesAgile Execution & Engineering Team Leadership - Lead a lean engineering team through PI planning, sprints, and releases - owning the backlog end to end and sequencing epics, features, and stories for speed and measurable business impact.
- Make execution trade-offs decisively across quality, scope, and timeline, and clear blockers rather than escalate them.
- Ensure release readiness through proactive coordination with engineering, QA, infrastructure, and design teams.
AI Product Ownership - Own AI capabilities end to end - from problem selection through production release, client adoption, and measured business value.
- Set the quality bar for every AI feature - accuracy targets, confidence thresholds, exception handling, and what the product does when the model gets it wrong - and maintain the evaluation sets that hold that bar as a release gate.
- Design human-in-the-loop workflows that keep an accountable person in the chain for anything touching the books, the ledger, or an investor deliverable.
- Drive document intelligence across the domain - extraction, classification, and validation of capital call and distribution notices, partnership agreements, statements, and investor correspondence.
- Shape agentic workflows that carry work across multiple steps rather than stopping at a single answer, and define clearly where the agent hands back to a person.
- Partner with data and engineering on the inputs AI depends on - document sources, master data quality, entitlements, and audit trail - and contribute to packaging, pricing, and enablement, including consumption-based models.
Client Relationship Management - Serve as the primary product point of contact for a portfolio of private equity clients and their finance, operations, and investor relations teams - running discovery, validating functionality, and demonstrating new capabilities on calls and on site.
- Recruit and run design partners for AI capabilities, proving value on real client data and real workflows before general release.
- Make the trust case with client teams and their auditors - how AI-assisted output is produced, reviewed, and evidenced - and be plain about what the product does not do.
Internal Collaboration & Stakeholder Alignment - Serve as a functional authority for fund accounting, investment operations, and investor relations in planning forums, executive reviews, and cross-team working sessions.
- Align with adjacent product and platform teams on shared components, AI services, integration points, and delivery sequencing - and with Sales and Customer Experience on what is landing when.
Domain Expertise & Technical Problem Solving - Apply deep understanding of private equity fund accounting and investment operations to inform product decisions - partnership accounting, capital allocations, management fees, waterfall and carried interest, capital calls and distributions, valuations, reconciliations, and period close.
- Bring working knowledge of the investor reporting lifecycle: capital account statements, quarterly reporting packages, ILPA-aligned templates, tax package coordination, and LP portal delivery.
- Judge where AI is genuinely the right tool and where deterministic logic, rules, or better workflow design is the stronger answer - particularly where numbers have to tie and results have to be defensible.
- Turn ambiguous client and business problems into scalable solutions, anticipating downstream system and client impact before committing to a design.
Impact Measurement & Continuous Improvement - Define success metrics before build rather than after, and monitor post-release performance against them.
- Instrument AI capabilities specifically - adoption, task completion, straight-through processing rate, correction and override rate, time saved, and consumption - and report on them honestly.
- Iterate on what is working, and rework or retire what is not earning its place.
Requirements- Minimum 5 years of product management experience within financial technology or investment management software.
- Demonstrated expertise in private equity fund accounting, investment operations, or investor relations workflows - capital activity, allocations, waterfalls, close, and investor reporting.
- Hands-on experience taking AI or machine learning capabilities to production in a commercial software product - shipped and adopted, not prototypes, demos, or internal pilots.
- Practical fluency with how modern AI systems actually behave: strengths and failure modes of large language models, retrieval, document extraction, evaluation, and the gap between a good demo and a dependable workflow.
- Proven track record of delivering product in a high-velocity, agile environment - sprint-driven, cross-functional, and execution-oriented.
- Strong client-facing skills with experience managing ongoing client relationships, conducting discovery sessions, and influencing product direction based on client input.
- Self-starter who takes ambiguous problems and moves them forward without waiting to be asked, with high execution autonomy inside a defined product domain.
- Exceptional cross-functional leadership and stakeholder communication capability across technical and non-technical audiences.
Preferred: - Experience at a fund administrator, GP finance team, or private markets software vendor.
- Familiarity with alternative investment platform technology and the operational needs of GPs, fund administrators, and institutional LPs.
- Experience with document intelligence, AI copilots, or agentic workflows in a regulated or audit-sensitive domain.
- Experience with consumption or usage-based pricing and packaging for software or AI capabilities.
- Experience working directly with engineering teams in a scaled agile or SAFe environment.
Technical and Systems Proficiency: - Backlog & Agile Planning Tools - Advanced (Jira, Azure DevOps, or equivalent)
- SQL - Intermediate to Advanced
- AI Tooling & Practice - Intermediate (prompt and context design, evaluation frameworks, reviewing and reasoning about model output; comfort with Claude, OpenAI, or equivalent)
- APIs & Integration Patterns - Intermediate (REST, webhooks, and emerging agent connectivity standards such as MCP)
- Product Analytics & Instrumentation - Intermediate (Pendo, Amplitude, or equivalent)
Education/Certifications- Bachelor's degree required; Accounting, Finance, Economics, Engineering, Computer Science, or related field preferred.
- Minimum 5 years of relevant experience in product ownership or product management within alternative investment or financial technology.
- Demonstrated full software development lifecycle delivery experience.
What We Offer- Health Coverage options along with other voluntary benefits
- Enterprise Udemy membership with access to thousands of personal and professional development courses
- 401K with Company match up to 4% or Employee Pension plan
- Competitive pay and year-end bonus potential
- Flexible PTO
- Charitable Donation matching, along with Volunteer and Voting PTO
- Numerous team building activities to promote collaboration in a fun and fast-paced work environment
