Mgr, Product Management - (GenAI/AI Product Experience)

Position Summary

Zora AI is Deloitte's AI agent platform delivering role-/function-specific products (e.g., Finance, Procurement, Supply Chain, Customer, Human Capital). As a Product Manager, you will own one or more sets of agent-enabled products end-to-end-defining vision, roadmap, requirements, and delivery-while ensuring trust, adoption, and measurable business outcomes for enterprise users.

Key Responsibilities:

• Own product strategy and roadmap: Define product vision, target users, value propositions, and multi-quarter roadmap across multiple role-/function-specific products.
• Translate needs into outcomes: Partner with clients/internal teams to identify high-value use cases, map workflows, and define "jobs to be done" and measurable success metrics.
• Lead discovery and delivery: Run discovery (research, prototypes, pilots) and delivery (MVP to scale), managing scope, tradeoffs, and dependencies across engineering, data, and design.
• Define product requirements: Create PRDs, user stories, acceptance criteria, and workflow diagrams for agent behaviors, tool integrations, and user experiences.
• Agent experience & orchestration: Specify agent capabilities (reasoning, task planning, tool use, approvals), human-in-the-loop patterns, and escalation/exception handling.
• Data and integration leadership: Drive requirements for connectors, data access patterns, security/privacy, logging/auditability, and integration with enterprise systems.
• Trustworthy AI & risk management: Partner with risk/compliance to address model governance, safety, monitoring, explainability, bias, and audit requirements.
• Go-to-market and enablement: Collaborate with sales and delivery to package offerings, define pricing/packaging inputs, create demos, and support pursuits and launches.
• Operate the product cadence: Maintain backlog, run sprint planning, track progress, and align stakeholders through clear decision points and communications.

• 7+ years of Product Management experience (enterprise software, SaaS, platforms, or data products), including shipping products from concept to GA.
• 2+ years of recent experience delivering products involving AI/ML (GenAI preferred), including evaluation, monitoring, and iteration loops.
• 2+ years of recent experience supporting product discovery (research, hypothesis testing, experimentation) and product delivery (requirements, backlog, release management).
• 1+ year working with enterprise integration patterns (APIs, eventing, identity/SSO, role-based access control, data pipelines).
• Limited immigration sponsorship may be available
• Ability to travel 0-10%, on average, based on the work you do and the clients and industries/sectors you serve

• Experience with agentic architectures (tool calling, retrieval-augmented generation, workflow orchestration, multi-agent patterns).
• Familiarity with LLM evaluation (quality metrics, red-teaming, grounding, hallucination mitigation) and observability.
• Domain depth in one or more target functions (e.g., Finance, Procurement, Supply Chain, HR, Customer Operations).
• Consulting, enterprise transformation, or platform product experience (shared services, reusable components, governance).
• Proven ability to manage multiple products with competing priorities and shared platform dependencies.
• Experience launching products with OCI / SAP / ERP / CRM ecosystems and connector marketplaces.
• Excellent stakeholder management and executive communication; able to write crisp narratives, PRDs, and decision memos.
• Track record of partnering with engineering, design, data science, and risk/compliance teams to deliver in regulated or high-stakes environments.

• Product strategy and 12-18 month roadmap with measurable outcomes.
• PRDs, epics, user stories, and acceptance criteria for each product/agent capability.
• Use-case catalog and prioritization model (value, feasibility, risk, readiness).
• MVP/pilot plans with success metrics, rollout phases, and scale criteria.
• Trust & governance artifacts: evaluation approach, monitoring plan, audit/logging requirements, and risk controls (in partnership with risk teams).
• Release plans and launch readiness checklists (docs, training, demo scripts, enablement).
• Customer feedback loop: telemetry dashboards, VOC insights, and iteration plan.

• Adoption: active users, repeat usage, workflow completion rates, feature utilization by product set.
• Business impact: cycle-time reduction for targeted workflows, cost-to-serve reductions, improved forecast accuracy or exception resolution time (by use case).
• Quality & reliability: task success rate, low rework/rollback rates, latency/uptime targets, incident trends.
• Trust & compliance: audit readiness, policy adherence, reduction in high-severity model risks, successful governance reviews.
• Delivery excellence: roadmap predictability, on-time releases, stakeholder satisfaction, reduced dependency blockers.
• Customer outcomes: pilot-to-scale conversion, referenceable wins, renewal/expansion influence (where applicable).

• Working model: Remote + Hybrid (2-3 days onsite) with flexibility based on team and client needs; operates in agile product teams with regular release cadence.
• Core stakeholders:
• Engineering (platform + product squads)
• Data Science / Applied AI (models, evaluation, tuning)
• Design / Research (UX, workflow design, prototyping)
• Cybersecurity & Privacy (security controls, data protection)
• Risk, Legal, Compliance (AI governance, auditability, policy alignment)
• Domain SMEs (Finance, Procurement, Supply Chain, HR, etc.)
• Sales, Alliances, and Delivery/Implementation (pursuits, packaging, rollout)
• Customer/Client stakeholders (product owners, process owners, IT, operations)