Best suited to candidates with AI-first operational experience working in SaaS or technology-driven environments, who are comfortable with cross-functional coordination and internal tooling development.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Cloudinary via Lever
About the Role
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.
Key Responsibilities:
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.
Qualifications & Experience:
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.
Source: Cloudinary careers (Lever)