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.
