AI Support Operations Lead

Job Summary

The AI Support Operations Lead is the technical architect of Mercans’ AI-agentic support model. This person designs, builds, deploys, and continuously improves the AI systems that handle L1 triage, automated resolution, knowledge base management, self-service, and support analytics. They work at the intersection of AI/ML engineering and support operations, working closely with the L3 team to convert root cause findings into AI resolution patterns, creating a virtuous cycle where human expertise continuously amplifies AI capability. This is a production-focused, outcomes-driven position — not a research role.

Duties & Responsibilities

AI System Design & Deployment

• Design and build the AI agentic support system integrated with YouTrack: automated categorization, priority assignment, routing, and auto-resolution where confidence thresholds are met
• Develop the client-facing self-service AI interface for common queries (payslip requests, form downloads, status checks) without ticket creation
• Monitor AI agent performance daily; retrain models, adjust confidence thresholds, and expand resolution coverage
• Manage AI-related vendor tools and APIs; evaluate new AI capabilities and recommend adoption

Knowledge Base & Training Data

• Build and maintain the knowledge base powering AI agents: structured resolution patterns, FAQ databases, client-specific runbooks, and compliance reference materials
• Work with L3 Engineers to convert their root cause findings and resolution patterns into AI training data and automated resolutions
• Establish the AI feedback loop: ensure 100% of human-resolved tickets are captured as potential AI training data

Analytics & Automation

• Implement real-time support analytics: ticket volumes, SLA health, AI triage accuracy, auto-resolution rates, bounce rates, recurring issue clusters, deployment impact
• Automate operational processes: SLA alerting, on-call notifications, status updates, ticket aging alerts, and weekly reporting
• Provide data-driven recommendations to the Head of Support on workload balancing, staffing needs, and process improvements

Collaboration

• Collaborate with the Customer Sentiment Pod on the proprietary AI agent roadmap
• Work with Support Engineers to capture resolution patterns from every human-resolved ticket

Skills & Qualifications

Required Competencies

• Proficiency with LLM architectures (Claude, GPT, custom models), NLP techniques, prompt engineering, RAG, and agent framework design
• Strong programming skills (Python, JavaScript/TypeScript)
• Experience with API integrations, database querying, and ITSM platform APIs (YouTrack, Jira)
• Data visualization capabilities (Grafana, Tableau, or equivalent)
• Understands support workflows, SLA management, and ticket lifecycle
• Able to translate operational pain points into automation solutions

Experience & Education

• Bachelor’s degree in Computer Science, Data Science, AI/ML, or related technical field. Advanced degree preferred
• Minimum 3 years in AI/ML engineering, support automation, or data engineering within a SaaS environment
• Direct experience building AI-powered support tools (chatbots, NLP categorization, auto-resolution systems) required
• Experience fine-tuning models on domain-specific data

SMART Goals

AI Auto-Triage Deployment

• Specific: Deploy AI auto-categorization and routing for all incoming support tickets across all clients
• Measurable: Percentage of tickets auto-categorized with 85%+ accuracy
• Achievable: Using LLM-based categorization trained on historical ticket data and client configurations
• Relevant: Eliminates manual queue management and enables the dedicated account model
• Time-bound: 100% coverage with 85%+ accuracy within 60 days of hire

Auto-Resolution Rate

• Specific: Achieve 20% auto-resolution rate on L1 tickets
• Measurable: Percentage of tickets fully resolved by AI without human intervention
• Achievable: By building resolution patterns from historical data and L3 documentation
• Relevant: Core metric for the lean, AI-first operating model
• Time-bound: 20% auto-resolution within 90 days of hire

Knowledge Base Foundation

• Specific: Build structured KB entries covering the top 50 recurring support issues
• Measurable: Number of KB articles indexed and searchable by AI
• Achievable: By analyzing historical ticket patterns and working with Support Engineers and L3 team
• Relevant: Foundation for AI auto-resolution and client self-service
• Time-bound: 50 KB articles within 75 days of hire

Analytics Dashboard

• Specific: Launch a real-time analytics dashboard covering all core KPIs
• Measurable: Dashboard operational with auto-refreshing data, reviewed in weekly meetings
• Achievable: Using existing ticket data from YouTrack and building automated data pipelines
• Relevant: Enables data-driven management of the support function
• Time-bound: Dashboard live within 30 days of hire

AI-L3 Feedback Loop

• Specific: Establish the process for converting L3 root cause closures into AI training data
• Measurable: Percentage of L3 closures fed into the AI learning pipeline
• Achievable: Working with L3 engineers to structure their findings into machine-readable patterns
• Relevant: Creates the flywheel where human expertise amplifies AI capability
• Time-bound: 100% of L3 closures fed to AI pipeline within 45 days of hire

Self-Service Pilot

• Specific: Launch an AI-powered self-service pilot for the top 3 query types (e.g., payslip visibility, form requests, status checks)
• Measurable: Pilot live and handling queries for at least 2 clients
• Achievable: Building on the KB foundation and AI triage infrastructure
• Relevant: Reduces ticket creation at the source
• Time-bound: Pilot live for 2+ clients within 90 days of hire