AI Operations Specialist (Customer Support)

• Map out intuitive, self-service conversation paths and resolution flows across messaging channels and web support, from first touch to resolution or smooth escalation.
• Define and maintain consistent agent personas, tone of voice, empathy guidelines, and brand-aligned dialogue tailored to various customer tiers and technical complexity.
• Architect conversational logic for ambiguous user intents, edge cases, and graceful failure handling.

• Formulate, refine, and maintain high-performing system prompts, few-shot examples, and dynamic context parameters.
• Work closely with knowledge bases and Retrieval-Augmented Generation (RAG) frameworks to ground AI answers in verified Sinch documentation.
• Implement robust boundary rules, safety filters, and anti-hallucination guardrails to protect customer data and uphold brand integrity.

• Build and run synthetic test suites, benchmark datasets, and regression checks to assess response accuracy, tone consistency, and task completion prior to production deployment.
• Continuously review real-world conversation logs, agent handoffs, and customer drop-offs to pinpoint failure patterns and areas of friction.
• Drive ongoing A/B testing on prompt variants, conversational hooks, and journey branches based on empirical CSAT, First Contact Resolution (FCR), and containment metrics.

• Collaborate with Support Engineering and IT to link conversational AI agents with core ticketing platforms (e.g., Zendesk), CRM systems, and internal Sinch APIs.
• Partner directly with front-line support managers and product teams to translate emerging product updates and user pain points into automated workflows.

What We Are Looking For

• Demonstrable experience structuring multi-turn dialogue trees, conversational UI flows, and tailored bot personas in an enterprise or SaaS setting.
• Strong command of prompt engineering techniques, LLM behavior, context windows, and strategies to minimize hallucination while maximizing resolution accuracy.
• Proven track record of using empirical data, conversation analytics, and structured evaluation rubrics to continuously benchmark and iterate on AI performance.
• Deep understanding of support operational metrics (CSAT, deflection/containment rates, Average Handle Time, escalation quality).

• Hands-on experience with conversational AI platforms (e.g., Sinch Chatlayer, Google Dialogflow, Cognigy) or modern generative AI agent builders.
• Familiarity with customer service ticketing systems (Zendesk, Jira Service Desk) and knowledge base management.
• Basic understanding of APIs, webhooks, and JSON data structures to understand how conversational agents pass and receive data.

• You let user interaction transcripts and performance data guide your design decisions.
• You stay ahead of rapid shifts in generative AI capabilities and are eager to test new approaches.
• Comfortable translating technical AI concepts into clear business logic for operational stakeholders.