AI product Manager -Digital

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Job Title: AI Product Manager Job Purpose You will be part of our GDS Assurance Digital, helping EY’s sector and service line professionals deliver AI products that improve audit quality, compress cycle times, and deliver measurable ROI at scale. You will work with multi-disciplinary teams across the entire region to support global clients, operating at the intersection of Assurance domain, engineering, data science/ML, design, risk, and global stakeholders.

You will own outcomes end-to-end—from shaping the problem space and defining a clear product vision, through to shipping resilient GenAI and agentic capabilities that are production-ready, measurable, and trusted. You feel comfortable driving and proposing approaches and methods to balance value, quality, risk and compliance, ensuring the product is instrumented for telemetry, experimentation, and cost/performance efficiency, and iterated rapidly based on evidence.

You are familiar with approaches, methods and tools for:

• Framing the problem space, defining product vision, crafting value hypotheses, and translating them into clear success metrics/OKRs
• Maintaining an evidence-based roadmap balancing foundational platform investments, high-impact features, and risk-reduction work
• Writing crisp PRDs and user stories that specify AI behaviours, guardrails, prompts/policies, data contracts, and evaluation criteria
• Leading cross-functional execution (engineering, DS/ML, design, risk) with strong sprint health, quality gates, and release readiness
• Operating disciplined Responsible AI governance (model cards, evaluation suites, red teaming, bias & safety checks, incident response, and release gates), and ensuring compliance with data residency/privacy and frameworks such as SOC2/ISO/NIST AI RMF
• You will build valued relationships with internal stakeholders and contribute to the development of a portfolio of business by focusing on high-impact opportunities. You will lead adoption and change through rollout plans, enablement materials, feedback programs, and stakeholder communications that drive durable usage across regions and teams.

Technical Skills Requirements

• Excellent academic background, including at a minimum a bachelor’s or master’s degree in Computer Science, Engineering, Data Science/AI, Information Systems, Business/Management, or a related field—with strong grounding in enterprise software delivery, data governance, security/privacy, and AI/ML fundamentals.
• 10+ years of work experience in AI Product Management, including 2+ years shipping LLM/GenAI capabilities in production, with focus on the following:
• AI product strategy & problem framing (translate ambiguous needs into observable, governable, cost-efficient capabilities)
• Technical feasibility & incubation (prototype → POC/POV → approved product closure)
• Evaluation & quality engineering for AI (quality, safety, latency, cost; offline/online evals; ownership of SLIs/SLOs)
• Platformization mindset (reusable components, prompt/policy management, feature flags, multi-tenant & multi-region readiness)
• Responsible AI operationalization across SDLC (security/risk/compliance partnership, rollback plans, kill switches)
• Adoption & change leadership (rollout plans, enablement, feedback loops, stakeholder communications to drive durable usage)
• Operational excellence (incident classification, on-call playbooks, post-incident reviews, staged/region-wise rollout governance)

Strong technical fluency to challenge decisions and trade-offs—without needing to author production code—across:

• Prompting & policy design, prompt/policy versioning, and guardrail patterns
• RAG and retrieval design (vectorization concepts, vector DB ecosystem awareness)
• Evaluation strategy (golden sets, regression suites, online monitoring; quality/safety/latency/cost optimization)
• Observability & telemetry (prompt/model call traces, tool usage, failure modes, per-tenant dashboards, cost controls)

Additionally, the following will be considered a strong asset:

• Familiarity with Azure for AI workloads (including Azure OpenAI) and the broader AI ecosystem (OpenAI API, vector DBs, guardrail frameworks)
• Hands-on with experimentat…