Product Manager, AI Transformation

eyewa; Dubai; On-site; AED 300,000 - 400,000; Full time

Job summary

eyewa in Dubai is seeking a Product Manager for their AI Transformation Squad. This role focuses on building AI-powered tools to enhance business operations across departments like marketing and supply chain.

The ideal candidate will have extensive experience in both product management and AI, capable of translating complex AI concepts into actionable business strategies. This full-time position emphasizes cross-functional collaboration and operational impact.

Qualifications

• 3+ years in product management, with 1–2 years owning AI or ML-powered products.
• Experience building AI tools that improved operations and reduced costs.
• Ability to gather requirements from multiple teams and prioritize effectively.

Responsibilities

• Define and own the AI transformation roadmap to align with business goals.
• Translate business problems into AI product specifications and requirements.
• Lead design discussions on model trade-offs and operational success metrics.

Skills

Product management

AI/ML product ownership

Cross-functional collaboration

Technical communication

Job description

Overview

AI Transformation Squad – Dubai, UAE – Full-time. We are hiring a Product Manager to lead the AI Transformation Squad, the team responsible for turning AI from a buzzword into a business lever at eyewa.

This is an execution role, not a strategy role. You will build AI-powered internal tools that eliminate manual work, reduce cost, and improve operational decisions across the business, working across Operations, CX, Marketing, Supply Chain, and Finance. You will also be eyewa’s internal authority on how AI gets shipped, defining the governance standards and best practices that every product team follows when productionising AI features.

Key Responsibilities

• Define and own the AI transformation roadmap across ops, CX, supply chain and data intelligence, sequenced by ROI with clear stakeholder alignment.
• Translate cross‑functional business problems into precise AI product specs, datasets, model objectives, output format, evaluation criteria, and integration points.
• Lead technical design discussions with ML engineers on trade‑offs between model approaches, latency, accuracy, and cost.
• Build, launch, and iterate on agentic workflows and intelligent automation that internal teams actually adopt and rely on.
• Own AI output quality in production: define what production‑ready means, build evaluation frameworks, and monitor accuracy, hallucination rate, latency, and reliability.
• Be the requirements funnel: collect and prioritise AI requests from across the business, push back on low‑ROI use cases, and protect ML engineering time.
• Define success metrics for every initiative before building: cost saved, time recovered, resolution rate, error reduction, NPS uplift.
• Own eyewa’s AI governance framework, output quality standards, evaluation criteria, monitoring requirements, explainability expectations, and production‑readiness protocols.
• Evangelise AI best practices across product teams, run enablement sessions, build playbooks, and act as the go‑to partner when any squad is productionising an AI feature.

Qualifications

• 3+ years in product management, with 1–2 years owning AI or ML‑powered products in a production environment.
• You've shipped something real that ran at scale, not a demo or a pilot that quietly died.
• Proven operational impact: you have built AI tools that replaced a manual process, reduced a real cost, or changed how a team works, and you can quantify the before/after.
• Technically credible with ML engineers: you understand prompt engineering, RAG architectures, agentic frameworks, and model trade‑offs well enough to have opinionated conversations.
• Business‑problem‑first thinker: you start with the workflow, not the technology, and can tell when a use case does not justify AI.
• Cross‑functional operator: you have gathered requirements from 5+ teams and prioritised a backlog across competing interests.
• Owns quality in production: reliability, accuracy, and user trust are part of your definition of done. You’ve built eval frameworks and iterated based on failure analysis.
• Clear communicator: you can explain a model’s output to a non‑technical ops manager and a business requirement to an ML engineer in the same day.
• Has a point of view on AI governance: you’ve defined standards, written playbooks, or run enablement sessions that actually changed how a team builds AI.

Nice to Have

• Experience building and deploying agentic workflows (LangChain, CrewAI, custom orchestration, or similar).
• Hands‑on experience with LLM evaluation frameworks, RAG, or fine‑tuning pipelines.
• Prior experience in eCommerce, retail, or omnichannel operations.
• Familiarity with MENA market dynamics and the operational realities of scaling in the region.
• Experience building decision‑support tools or dashboards that replaced manual reporting workflows.