Software Development Senior Analyst

Sensation Label:
General

This role supports both AI-enabled and human-assisted customer interactions by configuring no-code AI workflows, monitoring performance, and analyzing customer interaction data to drive continuous improvement, operational efficiency, and positive customer outcomes within a contact center environment.

• Design, build, and continuously evolve no-code AI agent journeys, including conversation flows, decision logic, and end-to-end user experiences.
• Configure intents, prompts, business rules, and escalation paths using intuitive tools, enabling scalable content management without direct coding.
• Collaborate with product owners, engineers, and business stakeholders to translate requirements into scalable, no-code agent experiences.
• Ensure all agent journeys align with governance, security, and responsible AI practices across the agent development lifecycle.

• Monitor and benchmark AI agent performance across journeys (accuracy, containment, resolution rate, user satisfaction), applying simulation-driven thinking to real-world scenarios.
• Analyze interaction logs and journey analytics to identify drop-offs, failure patterns, and optimization opportunities, feeding insights into continuous improvement loops.
• Design and maintain advanced search and query frameworks (speech and text, pattern-based logic) to enable automated analysis and topic identification in customer interactions.
• Build and maintain advanced speech and text queries (including pattern-based logic) to monitor both human and AI-assisted interactions.
• Generate actionable insights from interaction data and communicate findings to CX stakeholders, supporting data-driven decision making.

• Own the end-to-end quality management framework for AI agents — defining quality standards, evaluation criteria, scoring rubrics, and pass/fail thresholds across all deployed agent journeys.
• Conduct systematic conversation reviews and audits of AI agent interactions, scoring responses for accuracy, tone, compliance, escalation appropriateness, and resolution quality.

Sensation Label:
General

• Develop and maintain a structured QA scorecard tailored to AI agent interactions, incorporating dimensions such as intent recognition accuracy, hallucination detection, knowledge retrieval relevance, and conversation coherence.
• Identify recurring quality defects, failure modes, and edge cases through interaction sampling and trend analysis — distinguishing between prompt-level, knowledge-level, and integration-level root causes.
• Establish and run calibration sessions with cross-functional stakeholders (product, engineering, CX) to ensure consistent quality evaluation standards across agent deployments.
• Track quality metrics over time (QA pass rate, critical defect rate, regression frequency) and report trends to leadership with clear improvement recommendations.

• Translate quality findings into structured, actionable feedback for AI Agent Engineers — providing specific examples, annotated conversation logs, and clear descriptions of expected vs. actual agent behavior.
• Maintain a prioritized defect and improvement backlog informed by QA findings, categorized by severity, frequency, and customer impact — collaborating with engineers to drive resolution.
• Participate in regular feedback loops with engineering, reviewing prompt refinements, knowledge base updates, and guardrail adjustments to validate that quality issues are resolved without introducing regressions.
• Develop and curate a library of gold‑standard conversation examples and failure‑case annotations that…