Head of Support

Mercans; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Mercans is seeking a Head of Support to lead an AI-first product support function across all Mercans product lines. This role drives ticket elimination through automation, root cause resolution, and self-service, while managing a lean team providing 24/7 global coverage.

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 adding headcount.

Qualifications

• Proven leadership in transforming support into proactive, engineering-minded orgs.
• Hands-on experience deploying AI/ML based support tools (AI triage, chatbots, auto-resolution engines).
• Strong knowledge of SLA management frameworks, ITSM platforms, and support analytics.
• Data-driven decision-making with dashboards and trend analysis.
• Executive-level communication with clients and cross-functional stakeholders.

Responsibilities

• Own AI-driven support strategy and roadmap for triage, auto-resolution, self-service, and predictive support.
• Lead deployment of AI-based ticketing/triage tools; move to intelligent automation.
• Evaluate/adopt AI/ML capabilities to increase auto-resolution and reduce human intervention.
• Collaborate with Customer Sentiment Pod on the AI agent roadmap.
• Ensure SLA performance across all clients and report trends to leadership.
• Directly manage L3 engineering team, AI Operations Lead, and Senior Support Engineer.

Skills

AI/ML leadership

SLA management

Data-driven decisions

Executive communication

Team leadership

Education

Bachelor's degree in Engineering, Computer Science, Information Technology

Master's degree or MBA a plus

Tools

YouTrack

Jira

Job description

Location: Remote — EMEA Timezone (UTC+0 to UTC+4)

Reports to: Director of Engineering

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:

• 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
• 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:

• 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:

• 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

2. 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

3. AI Auto-Resolution Rate

• 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

4. 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 a global client base across multiple time zones
• Time-bound: Full 24/7 coverage operational within 45 days of hire
• 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

6. Month-over-Month Ticket Reduction

• Specific: Achieve sustained monthly ticket creation reduction
• Measurable: Monthly ticket creation count compared to the prior month
• Achievable: Through the 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.