Product Manager

Overview:

Inception42, a G42 company, is the region’s leading innovator of AI-powered domain-specific as well as industry-agnostic products, built on a rich heritage of research and development. Within the G42 ecosystem, Inception42 functions as the core intelligence layer – transforming data and compute infrastructure into real-world, applied AI solutions. Beyond its commercial endeavors, Inception42 is committed to creating positive societal impact. For more information, please visit www.inceptionai.ai

Overview

We are looking for a Product Manager (Cross-Module) to own the roadmap across our Investment AI product’s investment lifecycle modules. You will be accountable for what gets built, in what order, and why across sourcing and screening, due diligence, investment committee, and portfolio monitoring.

You will convert investment workflows and client evidence into clear product requirements, prioritize across AI, engineering, and design, and balance client needs with architectural constraints and commercial priorities. The role involves building one coherent enterprise SaaS platform rather than a set of separately conceived tools.

This is a permanent, full-time role based in Abu Dhabi, working closely with engineering, AI, design, domain, growth, delivery, and client teams. The environment is execution-focused and technically engaged, with travel across the GCC for client discovery and deployment support.

Responsibilities:

What You’ll Own

• Own and publish within the first three months a single, sequenced roadmap across sourcing and screening, due diligence, investment committee, and portfolio monitoring, making cross-module dependencies explicit early.
• Set and defend priorities, make trade-offs visible to leadership, and document why requests do or do not earn a place on the roadmap.
• Maintain a coherent product narrative and shared conventions so users experience our product as one platform across the investment lifecycle.
• Define, instrument, and report product measures for each module across adoption, workflow completion, and output quality, and use the evidence in prioritization.
• Translate investment workflows and client evidence into actionable requirements with clear acceptance criteria, edge cases, and non-functional expectations.
• Own the backlog and delivery cadence with engineering leads, establishing predictable releases and a materially improved match between committed and delivered scope by month six.
• Collaborate closely with design on workflows that reflect how investment professionals review, annotate, and defend an artefact, not simply read it.
• Own release readiness across configuration, migration of existing client data, documentation, and internal enablement for client-facing teams.
• Make informed product decisions within the platform architecture and engage directly in technical trade-off discussions with engineering and AI teams.
• Define expected behavior for AI-driven features, including grounding, citation, uncertainty, incomplete source material, and failure handling.
• Work with the AI team to define evaluation criteria and assess output quality before release rather than relying on subjective review after launch.
• Protect enterprise requirements that institutional buyers test directly, including access control, auditability, data residency, and performance at real document volumes.
• Bring commercial reasoning into prioritization by weighing pipeline impact, renewal risk, delivery cost, and user benefit.
• Maintain an evidence-based view of competing platforms and adjacent tools, using it to sharpen differentiation without chasing features.
• Support pricing, packaging, and positioning decisions with usage evidence and a clear view of the value each module creates for clients.
• Align engineering, AI, design, domain, growth, delivery, and client stakeholders around one current plan; run direct discovery with clients and design partners and convert the evidence into prioritized requirements.
• Improve requirements quality measurably by reducing rework and clarification cycles between product and engineering.
• Ship at least one significant cross-module capability into active client use within the first year, with clear adoption evidence attached.

Qualifications:

What We’re Looking For

• 8+ years in product management, including several years owning a B2B or enterprise SaaS product end to end rather than executing a roadmap set elsewhere.
• Product experience in financial services, investment technology, or a data or research platform serving professional investors.
• Demonstrated ownership of a roadmap across multiple modules, teams, or product areas, with evidence of prioritizing under genuine constraint.
• Experience working directly with engineering on a modern web and API stack, with the technical fluency to hold credible architecture and delivery trade-off discussions.
• A track record of shipping products into regulated or otherwise demanding enterprise environments where security, audit, and access control are procurement conditions.
• Strong requirements craft and written communication, with product documents that clarify decisions, edge cases, and failure behavior without unnecessary follow-up.
• Ability to work directly with domain experts and clients, absorb complex investment workflows, challenge assumptions constructively, and translate evidence into priorities.
• Strong commercial and product judgment, connecting roadmap choices to user value, pipeline, renewal risk, delivery cost, and platform coherence.
• Cross-functional leadership that aligns engineering, AI, design, and commercial colleagues around one plan without relying solely on formal authority.
• Bachelor’s degree in a technical, business, or quantitative discipline, or equivalent practical experience.

Nice to Have

• Hands-on experience shipping AI or machine-learning features, including retrieval-based systems, evaluation frameworks, and the handling of model error in professional workflows.
• Familiarity with Azure or an equivalent major cloud platform, including document-heavy ingestion and search challenges.
• Experience in an early-stage or scaling product organization where the product operating model had to be established rather than inherited.
• Working knowledge of the private capital deal lifecycle.