Product Support Solutions Developer

Join our team as a Product Support Solutions Developer, where you will design, build, and maintain the technical infrastructure and tools that power our customer support function. You will be responsible for custom tooling and integration development, AI agent and automation implementation, support operations and tool management, data engineering, analytics and reporting, and technical enablement and process improvement. This role requires hands-on programming experience, deep knowledge of customer support ecosystems, strong SQL skills, and excellent communication abilities.

• Design, build, and maintain custom applications, scripts, and webhook integrations across core support systems.
• Lead the evaluation, development, and deployment of AI agents, LLM-powered assistants, and chatbots to automate tier-1 support interactions.
• Administer, configure, and optimize support systems, maintaining SLAs, routing rules, triggers, and automated workflows.

• Development & Scripting: Hands-on programming experience (e.g., Python, JavaScript/Node.js, REST APIs, Webhooks, JSON) to build custom tooling and integrations
• Platform Expertise: Deep administrator/developer experience with customer support ecosystems (e.g., Zendesk API/Apps Framework, Jira Service Management, Salesforce, Freshdesk)
• Data & Analytics: Strong SQL skills and experience building dashboards in BI tools (e.g., Domo, Tableau, Looker) or native reporting engines (Zendesk Explore)
• Communication: Strong cross-functional collaboration skills to liaise effectively between Engineering, Product, and Support teams
• Experience: 3+ years of experience in product support engineering, support operations, technical support, or developer relations
• Operations & Enablement: Proven ability to document SOPs, lead technical training, and optimize operational support workflows
• AI & Automation Experience: Hands-on experience developing or implementing AI Agents, LLM APIs (e.g., OpenAI, Anthropic), conversational AI frameworks, or agentic frameworks (e.g., LangChain, AutoGen)
• Knowledge Management for AI: Experience with vector databases, prompt engineering, or managing knowledge bases optimized for AI search/RAG architectures
• Best Practices: Familiarity with ITIL framework, Agile methodology, or DevOps deployment practices