Job Summary
The Head of Support is a strategic leadership role responsible for building and operating an AI-first product support function across all Mercans product lines. This person owns the vision of ticket elimination through automation, root cause resolution, and self-service. They manage a lean team that includes a dedicated L3 engineering bench, an AI operations specialist, and a shift-based support engineering team providing 24/7 global coverage. The role is accountable for SLA delivery, customer satisfaction, team capability development, and cross-functional alignment with Product, Engineering, Compliance, and Configuration teams. The ideal candidate combines deep support operations experience with hands-on AI/automation knowledge and a proven track record of reducing support volume rather than scaling headcount.
Duties & Responsibilities
AI Strategy & Automation
Own the AI agentic support strategy: define the roadmap for AI-driven triage, auto-resolution, self-service, and predictive support
Lead the deployment of AI-based ticketing and triage tools, transitioning from manual queue management to intelligent automation
Evaluate and adopt AI/ML capabilities to continuously increase auto-resolution rates and reduce human intervention
Work with the Customer Sentiment Pod on the proprietary customer support AI agent roadmap
SLA Governance & Performance Management
Accountable for SLA performance across all clients; report on SLA adherence, breach root causes, and ticket volume trends to the Director of Engineering
Drive continuous ticket volume reduction through root cause elimination via the L3 team, product feedback loops, and AI self-service
Define and track KPIs: ticket volume trend, first-contact resolution, AI auto-resolution rate, mean time to resolution, reopen rate, L3 root cause closure rate, customer satisfaction
Conduct weekly performance reviews analyzing volumes, bounce rates, recurring issue patterns, and deployment impact
Team Leadership & Development
Directly manage the L3 engineering team, AI Operations Lead, and Senior Support Engineer; ensure the three layers work as an integrated system
Hire, develop, and retain a lean team of support engineers with technical depth and AI literacy
Design and maintain the 24/7 coverage model, balancing shift rotation, AI agent coverage, and on-call protocols
Champion a culture where every ticket is an opportunity to improve the product, the knowledge base, or the AI
Cross-Functional Accountability
Build cross-functional accountability with Product, Engineering, Compliance, and Configuration teams to ensure root cause fixes are delivered and sustained
Manage executive-level customer escalations; serve as the final escalation point before Director-level involvement
Approve and oversee the client allocation matrix, ensuring workload equity and alignment with client tier and complexity
Skills & Qualifications
Required Competencies
Proven leadership in transforming reactive support teams into proactive, engineering-minded organizations
Hands-on experience deploying AI/ML-based support tools (AI triage, chatbots, auto-resolution engines, NLP categorization)
Strong knowledge of SLA management frameworks, ITSM platforms (YouTrack, Jira, or similar), and support analytics
Data-driven decision-making: ability to build and interpret performance dashboards, identify trends, and translate metrics into actionable plans
Executive-level communication skills; comfortable presenting to clients, C-suite, and cross-functional stakeholders
Experience managing mixed teams (AI specialists, L3 engineers, support engineers)
Experience & Education
Bachelor’s degree in Engineering, Computer Science, Information Technology, or related technical field. Master’s degree or MBA a plus
Minimum 7 years in SaaS product support or service delivery, with at least 3 years leading a support team
Proven track record of reducing ticket volumes, not just scaling headcount
Experience in global payroll, HCM, or fintech SaaS strongly preferred
Familiarity with LLM-based agent architectures and prompt engineering
SMART Goals
Ticket Backlog Reduction
Specific: Reduce the total open ticket backlog across all product support clients
Measurable: Track total open ticket count weekly
Achievable: Through AI triage deployment, L3 root cause elimination, and dedicated account model enforcement
Relevant: Directly impacts SLA compliance, customer satisfaction, and team workload sustainability
Time-bound: Reduce from 147 to under 60 within 90 days of hire
AI Auto-Triage Deployment
Specific: Achieve full AI auto-categorization and routing on all incoming support tickets
Measurable: Percentage of tickets auto-categorized and routed by AI
Achievable: Working with the AI Operations Lead to deploy and tune the AI triage system
Relevant: Eliminates manual queue management and enables the dedicated account model
Time-bound: 100% of new tickets passing through AI triage within 60 days of hire
AI Auto-Resolution Rate
Specific: Achieve 30% auto-resolution rate on L1/L2 tickets
Measurable: Percentage of tickets resolved by AI without human intervention
Achievable: By building on the knowledge base, resolution patterns, and L3 root cause documentation
Relevant: Core to the lean, AI-first operating model
Time-bound: 30% auto-resolution within 90 days of hire
24/7 Coverage Model
Specific: Establish a fully operational 24/7 coverage model with zero gaps
Measurable: Documented shift schedule covering all 168 weekly hours with no uncovered periods
Achievable: Through shift rotation among support engineers combined with AI always-on coverage
Relevant: Required for global client base across multiple time zones
Time-bound: Full 24/7 coverage operational within 45 days of hire
L3 Root Cause Elimination
Specific: L3 engineering team delivering permanent root cause closures for recurring issues
Measurable: Number of root causes permanently resolved and verified in production
Achievable: Through the 3-person L3 team investigating and coordinating fixes with Product/Engineering
Relevant: Directly reduces ticket creation volume at the source
Time-bound: 20 root causes permanently eliminated within 90 days of hire
Month-over-Month Ticket Reduction
Specific: Achieve sustained monthly ticket creation reduction
Measurable: Monthly ticket creation count compared to prior month
Achievable: Through combined effect of AI auto-resolution, L3 root cause work, and improved deployment quality
Relevant: The primary measure of support function effectiveness in the AI-first model
Time-bound: 20% reduction sustained for 2 consecutive months within 90 days of hire