AI Product Manager

Nasze wymagania: Education & experience: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. Tech experience: Experience in Multi-Agent Systems (MAS) initiatives. Ability to effectively use AI tools in design processes. You have experience of creating and implementing data and artificial intelligence products. Soft skills: Excellent communication and influencing skills across technical and executive audiences. Good communication and teamwork skills, with the ability to explain technical ideas to diverse audiences. Experience working in collaborative, cross-functional environments. Proficiency in both spoken and written English is required. O projekcie: We are seeking an experienced AI Product Manager to design and implement Proof of Concepts (PoCs) for Multi-Agent Systems (MAS) that will drive innovation in enterprise AI applications. This role involves building intelligent agents, integrating them into distributed environments, and collaborating with cross-functional teams to deliver scalable AI solutions. Zakres obowiązków: Lead discovery: conduct stakeholder interviews and current‑state mapping (SIPOC/Swimlane/BPMN), with bottleneck/root‑cause analysis to surface high‑value MAS opportunities. Translate insights into product artifacts: craft clear problem statements, PRDs, user stories, acceptance criteria, and success metrics (OKRs/KPIs). Own lifecycle delivery: maintain and prioritize the backlog across PoC → MVP → Production. Define MAS system design: specify multi‑agent architectures and orchestration patterns (task decomposition, routing, tool/API use, memory/knowledge access, guardrails) aligned with platform standards. Leverage platform/CoE: partner to use approved models, frameworks, and components; publish reusable process maps, playbooks, and templates (PRDs, test plans, adoption guides). Drive agile execution: run ceremonies, manage timelines, coordinate cross‑functional contributors (data, ML, application, security), and remove delivery blockers. Lead readiness & adoption: own UAT strategy, change management, enablement, and rollout plans to achieve measurable outcomes. Ensure trusted data: coordinate with data owners for secure access, data quality, lineage, and integration to systems of record (CRM/ERP/MDM). Embed responsible AI: enforce safety, privacy, fairness, and auditability in alignment with governance and compliance. Measure & communicate impact: track cycle‑time, accuracy, data‑quality, adoption, and cost‑to‑serve; iterate continuously; serve as single‑threaded owner reporting status, risks, and decisions to sponsors and technical leadership.