Senior Knowledge and Quality Specialist

• Own the knowledge creation pipeline: maintain a prioritized backlog of knowledge that needs to be created, updated, or retired based on case volume, deflection gaps, RCA findings, and agent feedback 

• Partner with Support managers to identify the most critical knowledge gaps and hold teams accountable for creating and reviewing content on time 

• Set and track SLAs for knowledge creation: articles should be drafted, reviewed, and published within defined timeframes; flag and escalate when those timelines slip 

• Ensure product-provided knowledge and release documentation is translated into agent-ready content in a timely manner; do not allow knowledge gaps to persist after product changes ship 

• Work with the AI program team to align KB article priorities: knowledge created for agent use should also feed AI deflection workflows, and vice versa 

• Maintain a knowledge health dashboard: coverage by product, article age, usage rates, and gaps identified through QA and support case patterns 

• Ensure agents complete product-provided training on time: own the tracking, reporting, and escalation process for training completion across the support organization 

• Partner with Support managers to build training completion into team operating rhythms; completion rates should be a managed metric, not an afterthought 

• Design and maintain onboarding programs for new support agents that reflect the current AI-assisted support environment 

• Develop supplemental training content where product-provided training is insufficient, outdated, or not agent-ready 

• Track training completion rates, knowledge assessment scores, and correlation with QA performance; use data to identify where training is working and where agents still have gaps 

• When new AI workflows or product changes ship, ensure agents are trained before they encounter the change in production — not after 

• Own the QA program: define evaluation frameworks, scoring rubrics, calibration processes, and audit cadences; ensure audits are happening on schedule, not just when time permits 

• Track QA throughput: how many interactions are being evaluated per agent, per team, per period; flag and escalate when cadence slips 

• Evolve QA rubrics as AI changes the case mix: as routine cases are deflected, human-handled interactions are harder and more complex, and QA standards must reflect that shift 

• Ensure QA findings are actioned: partner with Support managers to close the loop between evaluation results and coaching; a QA program that produces scores without behavior change is not working 

• Connect QA findings to the knowledge pipeline: recurring failure patterns on specific interaction types should trigger KB article creation or training content updates 

• Produce regular QA reporting for Support leadership: score trends by agent, team, product, and case type; distinguish systemic issues from individual coaching needs 

• Ensure QA rubrics account for AI-assisted agent interactions: how agents use suggested responses, when they override AI recommendations, and how they handle AI-to-human handoffs 

• Serve as the Knowledge & Quality voice in the AI program: ensure knowledge and QA requirements are factored into AI development specifications from the start, not retrofitted after launch 

• Partner with the Director, CX Operations to identify where agent knowledge gaps are driving poor AI escalation handling; translate those gaps into KB priorities and training initiatives 

• When AI capabilities change what agents do, own the knowledge and training response: update content, retrain affected teams, and update QA rubrics to reflect new expectations 

• Contribute Knowledge & Quality data to the unified support AI scorecard: KB coverage, training completion rates, knowledge assessment scores, and QA cadence metrics 

• This role does not have direct reports at launch; the Senior Knowledge & Quality Specialist owns QA and Knowledge functions directly 

• Build collaborative relationships with Support managers so that knowledge creation and QA are seen as shared priorities, not external mandates 

• Partner with Support managers to ensure QA is used as a development tool, not a surveillance mechanism 

REQUIREMENTS 

Education & Certification 

• B.A. or B.S. degree required 

• Certification in instructional design, knowledge management, or quality management preferred 

Experience 

• 4+ years of experience in support knowledge management, QA, or training roles, with demonstrated ownership of program outcomes 

• Experience owning a knowledge creation pipeline: setting priorities, tracking completion, and holding teams accountable for producing content on time 

• Experience owning a QA program end to end: framework design, audit execution, reporting, and ensuring findings drive behavior change 

• Experience ensuring training completion and accountability in a support or contact center environment 

• Experience in an environment where AI or product changes required rapid knowledge and training updates; strongly preferred 

• Experience connecting KB content, training programs, and QA rubrics into a coherent agent knowledge ecosystem 

• SaaS, PE-backed technology, or government/public sector software experience preferred 

Skills 

• Knowledge program management: able to maintain a prioritized backlog, set SLAs, track completion, and escalate delays 

• QA program design and execution: able to build evaluation frameworks that are rigorous, consistent, and actioned by managers and agents 

• Training accountability: able to drive completion rates as a managed metric, not just deliver content and hope for the best 

• Cross-functional influence: able to hold Support managers accountable for knowledge creation and training completion without direct authority over them 

• Data fluency: able to analyze KB coverage gaps, QA trends, training completion rates, and assessment scores to identify patterns and drive decisions 

• Clear written and verbal communication; able to present findings to Support leadership and escalate risks to the Director, CX Operations 

Preferred 

• Familiarity with LMS platforms and knowledge base systems (e.g., Salesforce Knowledge, Confluence, or equivalent) 

• Experience designing QA rubrics for AI-assisted agent interactions 

• Experience in public safety, public administration, or mission-critical government software 

• Familiarity with Centerline, Forethought, or similar AI support platforms from a knowledge and QA perspective 

WHAT SUCCESS LOOKS LIKE 

In the first 90 days: 

• Complete a full audit of existing KB coverage, training programs, QA frameworks, and completion rates; identify the most critical gaps 

• Establish a working relationship with each Support manager; understand where knowledge creation is stalling, where training is not being completed, and where QA is inconsistent