project manager（英语产品经理）.

• Project Management (Primary Scope):
  • Cross-Functional Delivery: Plan and lead end-to-end project execution across engineering, platform/infrastructure, and operations teams from kickoff through release.
  • Release Lifecycle Management: Own release readiness, issue triage, version closeout, and release-note generation within Jira or equivalent tools to ensure high delivery quality.
  • Stakeholder & Dependency Coordination: Act as the primary liaison across teams to surface cross-functional dependencies, risks, and blockers early and drive resolution.
  • Execution & Communication: Facilitate project ceremonies (standups, status reviews, go/no-go gates), maintain clear status reporting, and drive data-informed decision-making.
• Product Management (Focused Scope):
  • Strategy & Roadmap: Define product vision, strategy, long-term roadmaps, and key metrics for the AI Platform.
  • Requirements & Definition: Gather requirements from internal stakeholders and translate them into actionable PRDs and key performance indicators.
  • Platform Adoption & Operations: Drive internal platform adoption across business units and collaborate on internal cost allocation/chargeback mechanisms.

skills and experience required.

• Mandatory Qualifications:
  • Project Management Experience: Proven experience as a Project Manager driving cross-functional delivery for developer platforms, infrastructure products, ML/AI platforms, or enterprise SaaS.
  • Release Lifecycle Ownership: Hands-on experience owning release readiness, version tracking, issue triage, and documentation using Jira or equivalent tooling.
  • Cross-Functional Collaboration: Demonstrated capability to coordinate with engineering, platform, and operations/SRE teams to deliver complex projects on schedule.
  • Product Management Competency: Experience as a Product Manager translating ambiguous requirements into clear PRDs, roadmaps, and metrics.
  • Core Soft Skills: Data-driven mindset accompanied by strong communication and interpersonal skills in a global, multicultural environment.
• Nice-to-Have Skills:
  • Familiarity with GPU/AI infrastructure concepts (e.g., Kubernetes, scheduling, multi-tenancy, inference serving, and MLOps).
  • Experience building and scaling internal platforms or driving product adoption across multiple internal teams.
  • Familiarity with LLM or generative AI infrastructure (training, inference, serving).