Mercans
Job Type: Permanent Location: Global (Remote) Job Summary The Team Lead Quality Assurance (QA) is responsible for designing, implementing, and managing QA frameworks, test strategies, monitoring, training, documentation, and client support for configuration deliverables in a global payroll SaaS environment. The role ensures high-quality, compliant, and consistent configurations through automation, robust testing, risk detection, and cross-functional collaboration. As a key member of the Configuration & Integrations department, this role leads all QA workstreams including automation, reporting, audits, training, and client engagement while driving continuous improvement, operational scalability, and stakeholder alignment. Key responsibilities: QA Strategy, Process Development & Governance: Design and maintain a comprehensive QA Framework aligned with ISO, SOC 2, and client-specific compliance requirements. Define and execute robust testing strategies for unit, functional, regression, UAT, and automated test scenarios. Implement standard templates, SOPs, coverage metrics, and audit-ready documentation practices. Integrate QA checkpoints throughout the configuration lifecycle and establish a QA Readiness Checklist for production deployment. Documentation & Standards Compliance: Design and maintain a comprehensive QA Framework aligned with ISO, SOC 2, and client-specific compliance requirements. Define and execute robust testing strategies for unit, functional, regression, UAT, and automated test scenarios. Implement standard templates, SOPs, coverage metrics, and audit-ready documentation practices. Integrate QA checkpoints throughout the configuration lifecycle and establish a QA Readiness Checklist for production deployment. Monitoring, RCA, and Risk Management: Deploy dashboards for real-time QA monitoring and test coverage tracking. Lead structured Root Cause Analyses (RCAs) and implement Corrective and Preventive Actions (CAPAs). Maintain a QA Risk Register and conduct early risk discovery workshops with Implementation and Product teams. Automation & Tooling: Partner with Product and Compliance to automate validation processes and reduce manual effort. Drive adoption of reusable test case libraries and standardized rules. Collaborate on AI/ML pilots to improve QA accuracy and predictive capabilities. Manage tools such as Jira, Zephyr, and TestRail for issue tracking and test management. Reporting & SLA Compliance: Define and manage QA reporting cadences including weekly status, monthly dashboards, and QBR inputs. Track performance against QA SLAs and KPIs (e.g., defect TAT, coverage rate, RCA closures). Analyze trends between internally identified vs. client-reported defects and publish insights. Client Success & Stakeholder Management: Represent QA in onboarding, UAT, go-live, and post-go-live governance meetings. Prepare QA briefings and audit logs for client handovers and escalations. Align with Client Success Managers to proactively prevent issues and ensure satisfaction. Training & Enablement: Develop and deliver tiered QA training programs (beginner to expert) and onboarding modules. Introduce internal QA certifications and peer review practices. Provide job aids, mentoring, and QA support to ensure consistent execution. Scalability & QA Maturity: Define a scalable QA operating model adaptable across countries, client tiers, and project scopes. Maintain a global QA knowledge base with reusable test cases and regional compliance insights. Lead QA maturity assessments and implement continuous improvement initiatives. Qualifications and Experience: Bachelors or Masters degree in Quality Assurance, Computer Science, Engineering, or a related technical or business field. Minimum 5 7 years of progressive experience in quality assurance roles within global payroll SaaS, HR tech, or complex compliance-driven platforms. Demonstrated leadership in designing and implementing QA frameworks, managing QA automation initiatives, and leading cross-functional QA efforts. Strong understanding of multi-country payroll processes, statutory compliance, configuration logic, and payroll software QA methodologies. Proven experience in managing QA documentation aligned with ISO 9001, ISO 27001, and SOC 2 certification requirements. Experience working with audit teams, responding to compliance reviews, and delivering evidence in formal certification processes. Familiarity with the application of AI/ML in QA automation or predictive defect management is preferred. Advanced skills in QA tools such as Jira, TestRail, Zephyr, and test automation platforms. Excellent analytical, stakeholder management, communication, and presentation skills, with the ability to influence both technical and non-technical audiences. SMART Performance Goals Global QA Framework Deployment Deliver a documented risk-based QA framework by Q3 2025. Achieve 100% implementation across configuration types and regions. Testing Documentation Compliance Finalize audit-ready QA documentation templates by Q2 2025. Ensure 95% documentation compliance in all tracked QA cycles. RCA and CAPA Execution Lead monthly Root Cause Analyses and implement Corrective and Preventive Actions. Resolve 90% of critical recurring issues within 60 days of identification. Client QA Engagement Participate in 100% of Tier 1 client UAT and go-live QA reviews. Maintain a client QA satisfaction score of 85% or higher post-implementation. QA Automation & AI Pilots Launch 2 QA automation tools and 1 AI/ML initiative for predictive testing by Q4 2025. Reduce manual QA time by 25% for the most frequently used configuration types. SLA Monitoring and Reporting Launch live QA dashboards and SLA scorecards by Q3 2025. Maintain 95% or higher adherence to internal QA SLAs. Internal QA Training & Certification Rollout Launch a structured QA onboarding and certification program for new and existing team members by Q2 2025. Certify 100% of QA staff and ensure 90% QA checklist compliance in project reviews. QA Knowledge Base & Global Reusability Library Develop and publish a centralized QA knowledge base by Q3 2025. Ensure 80% of projects utilize reusable QA assets, templates, or test libraries by Q4 2025.
Job Type: Permanent Location: Global (Remote) Job Summary The Team Lead Quality Assurance (QA) is responsible for designing, implementing, and managing QA frameworks, test strategies, monitoring, training, documentation, and client support for configuration deliverables in a global payroll SaaS environment. The role ensures high-quality, compliant, and consistent configurations through automation, robust testing, risk detection, and cross-functional collaboration. As a key member of the Configuration & Integrations department, this role leads all QA workstreams including automation, reporting, audits, training, and client engagement while driving continuous improvement, operational scalability, and stakeholder alignment. Key responsibilities: QA Strategy, Process Development & Governance: Design and maintain a comprehensive QA Framework aligned with ISO, SOC 2, and client-specific compliance requirements. Define and execute robust testing strategies for unit, functional, regression, UAT, and automated test scenarios. Implement standard templates, SOPs, coverage metrics, and audit-ready documentation practices. Integrate QA checkpoints throughout the configuration lifecycle and establish a QA Readiness Checklist for production deployment. Documentation & Standards Compliance: Design and maintain a comprehensive QA Framework aligned with ISO, SOC 2, and client-specific compliance requirements. Define and execute robust testing strategies for unit, functional, regression, UAT, and automated test scenarios. Implement standard templates, SOPs, coverage metrics, and audit-ready documentation practices. Integrate QA checkpoints throughout the configuration lifecycle and establish a QA Readiness Checklist for production deployment. Monitoring, RCA, and Risk Management: Deploy dashboards for real-time QA monitoring and test coverage tracking. Lead structured Root Cause Analyses (RCAs) and implement Corrective and Preventive Actions (CAPAs). Maintain a QA Risk Register and conduct early risk discovery workshops with Implementation and Product teams. Automation & Tooling: Partner with Product and Compliance to automate validation processes and reduce manual effort. Drive adoption of reusable test case libraries and standardized rules. Collaborate on AI/ML pilots to improve QA accuracy and predictive capabilities. Manage tools such as Jira, Zephyr, and TestRail for issue tracking and test management. Reporting & SLA Compliance: Define and manage QA reporting cadences including weekly status, monthly dashboards, and QBR inputs. Track performance against QA SLAs and KPIs (e.g., defect TAT, coverage rate, RCA closures). Analyze trends between internally identified vs. client-reported defects and publish insights. Client Success & Stakeholder Management: Represent QA in onboarding, UAT, go-live, and post-go-live governance meetings. Prepare QA briefings and audit logs for client handovers and escalations. Align with Client Success Managers to proactively prevent issues and ensure satisfaction. Training & Enablement: Develop and deliver tiered QA training programs (beginner to expert) and onboarding modules. Introduce internal QA certifications and peer review practices. Provide job aids, mentoring, and QA support to ensure consistent execution. Scalability & QA Maturity: Define a scalable QA operating model adaptable across countries, client tiers, and project scopes. Maintain a global QA knowledge base with reusable test cases and regional compliance insights. Lead QA maturity assessments and implement continuous improvement initiatives. Qualifications and Experience: Bachelors or Masters degree in Quality Assurance, Computer Science, Engineering, or a related technical or business field. Minimum 5 7 years of progressive experience in quality assurance roles within global payroll SaaS, HR tech, or complex compliance-driven platforms. Demonstrated leadership in designing and implementing QA frameworks, managing QA automation initiatives, and leading cross-functional QA efforts. Strong understanding of multi-country payroll processes, statutory compliance, configuration logic, and payroll software QA methodologies. Proven experience in managing QA documentation aligned with ISO 9001, ISO 27001, and SOC 2 certification requirements. Experience working with audit teams, responding to compliance reviews, and delivering evidence in formal certification processes. Familiarity with the application of AI/ML in QA automation or predictive defect management is preferred. Advanced skills in QA tools such as Jira, TestRail, Zephyr, and test automation platforms. Excellent analytical, stakeholder management, communication, and presentation skills, with the ability to influence both technical and non-technical audiences. SMART Performance Goals Global QA Framework Deployment Deliver a documented risk-based QA framework by Q3 2025. Achieve 100% implementation across configuration types and regions. Testing Documentation Compliance Finalize audit-ready QA documentation templates by Q2 2025. Ensure 95% documentation compliance in all tracked QA cycles. RCA and CAPA Execution Lead monthly Root Cause Analyses and implement Corrective and Preventive Actions. Resolve 90% of critical recurring issues within 60 days of identification. Client QA Engagement Participate in 100% of Tier 1 client UAT and go-live QA reviews. Maintain a client QA satisfaction score of 85% or higher post-implementation. QA Automation & AI Pilots Launch 2 QA automation tools and 1 AI/ML initiative for predictive testing by Q4 2025. Reduce manual QA time by 25% for the most frequently used configuration types. SLA Monitoring and Reporting Launch live QA dashboards and SLA scorecards by Q3 2025. Maintain 95% or higher adherence to internal QA SLAs. Internal QA Training & Certification Rollout Launch a structured QA onboarding and certification program for new and existing team members by Q2 2025. Certify 100% of QA staff and ensure 90% QA checklist compliance in project reviews. QA Knowledge Base & Global Reusability Library Develop and publish a centralized QA knowledge base by Q3 2025. Ensure 80% of projects utilize reusable QA assets, templates, or test libraries by Q4 2025.
Mercans
Job Type: Permanent Location: APAC Job Summary The Senior DevOps Engineer serves as a critical execution layer for Mercans AI-native infrastructure strategy and broader product/engineering delivery and automation. Either based in Tartu, Estonia or Remote, reporting directly to the CTO, and collaborating with Product Managers, Engineering Managers, software engineers, data scientists, and SRE teams, this role operationalizes the technical vision through hands-on automation, GitLab DevSecOps pipelines, and resilient platform operations. The position focuses on building and maintaining a secure, cost-efficient private cloud environment capable of hyperscale payroll processing and proprietary AI model training/inference, while providing deployment automation, feature flagging, and release orchestration to accelerate Product and Engineering team velocity. Duties and Responsibilities: Platform Automation & CI/CD Support Product and Engineering Teams by implementing and maintaining GitLab CI/CD pipelines that enable rapid feature delivery, A/B testing, feature flagging, and blue-green deployments across all product lines, enforcing architectural standards, security controls, and AI-first engineering patterns defined by the Enterprise Architecture Board. Provide deployment automation for Product releases with GitLab CI/CD pipelines including shift-left security (SAST, DAST, dependency scanning, container scanning, IaC scanning, secret detection) integrated into merge requests, environment promotion gates, and production deployment approvals. Enable Engineering velocity through self-service deployment templates, environment provisioning APIs, and GitLab pipeline libraries that reduce cognitive load for application teams building payroll/HR features. Automate Product experimentation with GitLab Feature Flags, progressive delivery patterns, and canary releases to enable Product Teams to test hypotheses with minimal deployment risk. Private Cloud Operations Automate infrastructure provisioning for the private cloud (Kubernetes, HCI, GPU nodes, storage) using Infrastructure as Code in line with the AI Cloud reference architecture, scanning Terraform/Kubernetes manifests for misconfigurations via GitLab. Operate and optimize GPU-enabled Kubernetes clusters, including bin-packing, autoscaling, and fractional GPU scheduling to support AI training and inference workloads efficiently, with GitLab runtime security policies and container image scanning for CVEs. Observability & Resilience Implement observability (logging, metrics, tracing) and SRE practices to contribute toward the 99.999% availability target and active-active multi-datacenter strategy for core payroll and AI services, leveraging GitLab security dashboards for vulnerability tracking and remediation. Identify operational issues, implement fixes and performance improvements, and contribute to chaos engineering and resilience drills to build an anti-fragile engineering culture, with GitLab conditional pipelines for secure testing and deployment. Security & Compliance Ensure systems are safe and secure against cybersecurity threats by embedding GitLab security policies into pipelines, managing secrets with detection scans, enforcing role-based access control (RBAC), and achieving policy compliance through MR approvals and dashboards. Work closely with Product Managers, software engineers, data scientists, and MLOps teams to standardize release processes for AI models and product features, reduce lead time to production, and integrate with model registries, compliance checks, and feature management platforms using GitLabs end-to-end DevSecOps workflows. Documentation & Knowledge Transfer Produce high-quality documentation for runbooks, deployment procedures, GitLab pipeline templates, and platform standards, and contribute to internal Centers of Excellence for SRE and AI Engineering, including GitLab security best practices training. Skills and experience 4 6+ years of experience as a DevOps / SRE / Platform Engineer operating production grade Kubernetes based systems and CI/CD pipelines. Hands on experience with private cloud or on prem Kubernetes (e.g., CAPI based clusters, HCI) and automation tools (Terraform/Ansible or equivalents). Experience running containerized workloads with GPUs, including familiarity with scheduling, resource quotas, and performance tuning for AI/ML workloads. Strong automation skills and programming ability in at least one language (e.g., Python, Go, or similar) for scripting, integrations, and tooling. Good understanding of observability stacks, incident management, and SRE practices (SLIs/SLOs, error budgets, postmortems). Knowledge of secure software delivery practices, secrets management, and compliance aware deployment in regulated or data sensitive environments. Proficiency with GitLab DevSecOps: Configuring (link removed) templates for SAST/DAST/dependency/container/IaC scanning, security dashboards, RBAC, policy enforcement, feature flags, and progressive delivery in CI/CD pipelines. Experience enabling Product/Engineering teams with self-service deployment platforms, GitOps workflows, and golden deployment paths that balance velocity and safety. Experience with Agile teams and collaborative ways of working across Product, development, architecture, and data/AI functions. Strong documentation, time management, and communication skills in English, with readiness to take initiative and shape DevOps practices from the ground up in alignment with architectural guidelines. Performance Goals: CI/CD reliability and speed Specific: Design and standardize GitLab CI/CD pipelines for core payroll, AI services, and Product feature releases, including automated security testing (SAST, DAST, scanning) and deployment approvals. Measurable: Achieve a pipeline success rate of at least 98% and reduce median lead time from commit to production to under 2 hours for target services and product features. Achievable: Leverage GitLab security templates and collaborate with Product, development, and QA teams to streamline stages and remove manual bottlenecks. Relevant: Directly supports AI first engineering standards and Product velocity for faster time to market for new features and AI models. Time bound: Target achieved by end of Q3 2026. Infrastructure cost and utilization optimization Specific: Implement bin packing strategies, right size workloads, and refine Kubernetes scheduling for CPU, memory, and GPU resources in the private cloud. Measurable: Contribute to a 15% reduction in infrastructure cost per payslip and increase average GPU and node utilization to at least 70% on production clusters. Achievable: Use monitoring data and autoscaling capabilities; coordinate with architecture on capacity planning and hardware lifecycle. Relevant: Supports broader COGS reduction and maximizes ROI on AI hardware investments. Time bound: Target achieved by end of Q4 2026. Platform resilience and incident reduction Specific: Implement SRE practices, incident runbooks, and active active aware deployment patterns for critical payroll, AI, and Product services. Measurable: Help reach 99.99%+ availability for owned services on the path to five nines for the core engine, and reduce high severity incidents (Sev 1 and Sev 2) by 30% year over year. Achievable: Introduce improved alerting, standardized playbooks, and participate in chaos drills and postmortems to address systemic issues. Relevant: Aligned with the strategic goal of enterprise grade resilience for Tier 1 clients. Time bound: Measured over the 12 month period following the hire date. MLOps and Product deployment velocity Specific: Integrate GitLab CI/CD pipelines with the model registry, compliance checks, and Product feature management, enabling automated deployment of AI models and product releases to the private cloud. Measurable: Reduce the lead time for deploying updated AI models and Product features from weeks to less than 24 hours for prioritized use cases, with zero non approved deployments. Achievable: Build pipeline templates for AI workloads and Product releases and collaborate with data science, Product, and GRC teams. Relevant: Supports the organizations target of advanced MLOps maturity and Product velocity with safe AI and feature adoption at scale. Time bound: Initial target achieved by Q4 2026, with continuous improvement thereafter. Operational excellence and knowledge sharing Specific: Create and maintain platform documentation, runbooks, and internal knowledge sessions focused on private cloud, GitLab DevSecOps, CI/CD, Product deployment patterns, and AI infrastructure operations. Measurable: Publish at least 10 high quality runbooks or platform guides and lead a minimum of 6 internal technical sessions or deep dives per year. Achievable: Integrate documentation and knowledge sharing into incident resolution, new feature rollout, and architectural change activities. Relevant: Strengthens internal Centers of Excellence and supports talent density and mentorship objectives. Time bound: Targets measured on an annual basis, with the first cycle ending Q2 2026.
Job Type: Permanent Location: APAC Job Summary The Senior DevOps Engineer serves as a critical execution layer for Mercans AI-native infrastructure strategy and broader product/engineering delivery and automation. Either based in Tartu, Estonia or Remote, reporting directly to the CTO, and collaborating with Product Managers, Engineering Managers, software engineers, data scientists, and SRE teams, this role operationalizes the technical vision through hands-on automation, GitLab DevSecOps pipelines, and resilient platform operations. The position focuses on building and maintaining a secure, cost-efficient private cloud environment capable of hyperscale payroll processing and proprietary AI model training/inference, while providing deployment automation, feature flagging, and release orchestration to accelerate Product and Engineering team velocity. Duties and Responsibilities: Platform Automation & CI/CD Support Product and Engineering Teams by implementing and maintaining GitLab CI/CD pipelines that enable rapid feature delivery, A/B testing, feature flagging, and blue-green deployments across all product lines, enforcing architectural standards, security controls, and AI-first engineering patterns defined by the Enterprise Architecture Board. Provide deployment automation for Product releases with GitLab CI/CD pipelines including shift-left security (SAST, DAST, dependency scanning, container scanning, IaC scanning, secret detection) integrated into merge requests, environment promotion gates, and production deployment approvals. Enable Engineering velocity through self-service deployment templates, environment provisioning APIs, and GitLab pipeline libraries that reduce cognitive load for application teams building payroll/HR features. Automate Product experimentation with GitLab Feature Flags, progressive delivery patterns, and canary releases to enable Product Teams to test hypotheses with minimal deployment risk. Private Cloud Operations Automate infrastructure provisioning for the private cloud (Kubernetes, HCI, GPU nodes, storage) using Infrastructure as Code in line with the AI Cloud reference architecture, scanning Terraform/Kubernetes manifests for misconfigurations via GitLab. Operate and optimize GPU-enabled Kubernetes clusters, including bin-packing, autoscaling, and fractional GPU scheduling to support AI training and inference workloads efficiently, with GitLab runtime security policies and container image scanning for CVEs. Observability & Resilience Implement observability (logging, metrics, tracing) and SRE practices to contribute toward the 99.999% availability target and active-active multi-datacenter strategy for core payroll and AI services, leveraging GitLab security dashboards for vulnerability tracking and remediation. Identify operational issues, implement fixes and performance improvements, and contribute to chaos engineering and resilience drills to build an anti-fragile engineering culture, with GitLab conditional pipelines for secure testing and deployment. Security & Compliance Ensure systems are safe and secure against cybersecurity threats by embedding GitLab security policies into pipelines, managing secrets with detection scans, enforcing role-based access control (RBAC), and achieving policy compliance through MR approvals and dashboards. Work closely with Product Managers, software engineers, data scientists, and MLOps teams to standardize release processes for AI models and product features, reduce lead time to production, and integrate with model registries, compliance checks, and feature management platforms using GitLabs end-to-end DevSecOps workflows. Documentation & Knowledge Transfer Produce high-quality documentation for runbooks, deployment procedures, GitLab pipeline templates, and platform standards, and contribute to internal Centers of Excellence for SRE and AI Engineering, including GitLab security best practices training. Skills and experience 4 6+ years of experience as a DevOps / SRE / Platform Engineer operating production grade Kubernetes based systems and CI/CD pipelines. Hands on experience with private cloud or on prem Kubernetes (e.g., CAPI based clusters, HCI) and automation tools (Terraform/Ansible or equivalents). Experience running containerized workloads with GPUs, including familiarity with scheduling, resource quotas, and performance tuning for AI/ML workloads. Strong automation skills and programming ability in at least one language (e.g., Python, Go, or similar) for scripting, integrations, and tooling. Good understanding of observability stacks, incident management, and SRE practices (SLIs/SLOs, error budgets, postmortems). Knowledge of secure software delivery practices, secrets management, and compliance aware deployment in regulated or data sensitive environments. Proficiency with GitLab DevSecOps: Configuring (link removed) templates for SAST/DAST/dependency/container/IaC scanning, security dashboards, RBAC, policy enforcement, feature flags, and progressive delivery in CI/CD pipelines. Experience enabling Product/Engineering teams with self-service deployment platforms, GitOps workflows, and golden deployment paths that balance velocity and safety. Experience with Agile teams and collaborative ways of working across Product, development, architecture, and data/AI functions. Strong documentation, time management, and communication skills in English, with readiness to take initiative and shape DevOps practices from the ground up in alignment with architectural guidelines. Performance Goals: CI/CD reliability and speed Specific: Design and standardize GitLab CI/CD pipelines for core payroll, AI services, and Product feature releases, including automated security testing (SAST, DAST, scanning) and deployment approvals. Measurable: Achieve a pipeline success rate of at least 98% and reduce median lead time from commit to production to under 2 hours for target services and product features. Achievable: Leverage GitLab security templates and collaborate with Product, development, and QA teams to streamline stages and remove manual bottlenecks. Relevant: Directly supports AI first engineering standards and Product velocity for faster time to market for new features and AI models. Time bound: Target achieved by end of Q3 2026. Infrastructure cost and utilization optimization Specific: Implement bin packing strategies, right size workloads, and refine Kubernetes scheduling for CPU, memory, and GPU resources in the private cloud. Measurable: Contribute to a 15% reduction in infrastructure cost per payslip and increase average GPU and node utilization to at least 70% on production clusters. Achievable: Use monitoring data and autoscaling capabilities; coordinate with architecture on capacity planning and hardware lifecycle. Relevant: Supports broader COGS reduction and maximizes ROI on AI hardware investments. Time bound: Target achieved by end of Q4 2026. Platform resilience and incident reduction Specific: Implement SRE practices, incident runbooks, and active active aware deployment patterns for critical payroll, AI, and Product services. Measurable: Help reach 99.99%+ availability for owned services on the path to five nines for the core engine, and reduce high severity incidents (Sev 1 and Sev 2) by 30% year over year. Achievable: Introduce improved alerting, standardized playbooks, and participate in chaos drills and postmortems to address systemic issues. Relevant: Aligned with the strategic goal of enterprise grade resilience for Tier 1 clients. Time bound: Measured over the 12 month period following the hire date. MLOps and Product deployment velocity Specific: Integrate GitLab CI/CD pipelines with the model registry, compliance checks, and Product feature management, enabling automated deployment of AI models and product releases to the private cloud. Measurable: Reduce the lead time for deploying updated AI models and Product features from weeks to less than 24 hours for prioritized use cases, with zero non approved deployments. Achievable: Build pipeline templates for AI workloads and Product releases and collaborate with data science, Product, and GRC teams. Relevant: Supports the organizations target of advanced MLOps maturity and Product velocity with safe AI and feature adoption at scale. Time bound: Initial target achieved by Q4 2026, with continuous improvement thereafter. Operational excellence and knowledge sharing Specific: Create and maintain platform documentation, runbooks, and internal knowledge sessions focused on private cloud, GitLab DevSecOps, CI/CD, Product deployment patterns, and AI infrastructure operations. Measurable: Publish at least 10 high quality runbooks or platform guides and lead a minimum of 6 internal technical sessions or deep dives per year. Achievable: Integrate documentation and knowledge sharing into incident resolution, new feature rollout, and architectural change activities. Relevant: Strengthens internal Centers of Excellence and supports talent density and mentorship objectives. Time bound: Targets measured on an annual basis, with the first cycle ending Q2 2026.