Product Operations Lead at Cloudinary (Israel)Product Operations Lead

We are seeking a Product Operations Lead to play a key role in product launches, planning processes, AI adoption and operational efficiency. This role will ensure product availability, process optimization, and KPI tracking, working cross-functionally with Product, Engineering, Marketing, and Customer Success teams. The ideal candidate will drive seamless product delivery, scale best practices, and enhance the product management lifecycle.

Product Launch & Go-to-Market Readiness

• Drive cross-functional product launch planning, ensuring clear responsibilities and execution timelines.
• Develop together with Product Marketing scalable launch playbooks to standardize and optimize the GTM process.
• Track and measure launch success through key metrics such as adoption, usage, and retention.
• Ensure internal teams are enabled with product documentation, updates, release notes and other enablement principles
• Partner with Customer Success to optimize post-launch support and user experience.
• Monitor product readiness and ensure alignment between Product, R&D, Support and GTM teams.

AI & Builder Approach

• Work AI-first - use Claude and the connected stack (JIRA, Confluence, Vitally, Gong, Slack) as the default method for drafting, analysis, and synthesis, not an occasional shortcut.
• Build the tooling you need. Ship lightweight internal tools - scripts, agents, digests, dashboards - that remove recurring manual work in release and launch operations, without waiting for an engineering roadmap slot.
• Contribute to Product Brain, our internal AI knowledge layer, and design operational workflows that pull context from connected systems rather than reassembling it by hand each cycle.
• Own AI adoption within Product Operations - find the manual work a system should be doing, prototype the replacement, and prove it works before asking anyone to adopt it.
• Apply judgment on where automation belongs. Know which decisions, escalations, and communications need a human, and keep those human.

KPI Tracking & Operational Excellence

• Define and track key performance indicators (KPIs) to measure release and launch effectiveness.
• Identify and implement process improvements to enhance speed and efficiency.
• Leverage data insights to drive continuous optimization of product operations workflows.
• Own and optimize tooling and dashboards to provide real-time visibility into product health.
• Partner with R&D to streamline deployment workflows, product maintenance and incident response guidelines.

Required Skills & Experience

• At least 7 years in Product Operations, Business Development or Product Lifecycle Management within a SaaS or technology-driven company.
• Experience with feature or Product delivery, value driven decision making and coordinating product launches and releases.
• Experience in  agile & kanban development, release processes, and change management.
• Hands-on daily use of AI tools in real work, with concrete examples of what changed as a result.
• Proficiency in JIRA, Confluence, or similar release tracking tools.
• Data-driven mindset with experience in tracking and analyzing product performance metrics, overseeing execution strategy and tactical improvements
• Comfortable contributing to engineering discussions around technology decisions and strategy related to a product pipeline
• Excellent communication, collaboration, and stakeholder management skills.

Desired Skills

• Knowledge of SaaS business models, pricing strategies, and GTM execution.
• Familiarity with customer adoption and retention analytics.
• Experience working with API-first or developer-focused products.
• Experience building internal tools or automations that other teams actually adopted.
• Familiarity with LLM-based workflows — prompt and agent design, MCP connectors, or similar.

Success Metrics

• Efficient and predictable releases, with minimal delays or incidents.
• Stronger cross-functional coordination, improving launch readiness.
• Standardized product operations processes, improving efficiency and scalability.
• Data-driven decision-making, leveraging insights to optimize release and launch performance.
• Higher product adoption rates, increasing user engagement and retention.
• Reduced manual operational load, with recurring coordination and reporting work replaced by AI-assisted workflows and internal tooling.