Conversational AI Engineer (Senior)

In this role you design, build, and operate voice agents and multi-turn conversational systems at scale to support customer and operational use cases. You own end-to-end delivery from conversation design and NLU integration to API wiring, observability, and production operations. You’ll collaborate with linguists, backend engineers, and product teams to raise overall capability with reusable components and AI-assisted tooling. This is a chance to shape production-grade conversational AI within a cross-functional, agile environment.

• Design, build, and operate voice agents and multi-turn dialogues with end-to-end quality ownership
• Architect dialogue logic including intent routing, context management, state handling, fallbacks, and escalation
• Integrate agents with backend services via REST APIs for live data and business processes
• Tune speech pipeline components (ASR, TTS, VAD) to meet accuracy and latency targets
• Instrument observability: conversation analytics, containment rates, drop-offs, error logging, and alerts for quality regressions
• Develop reusable dialogue components and platform patterns to accelerate delivery
• Collaborate with Computational Linguists to improve NLU quality, intent coverage, and entity extraction
• Diagnose issues across dialogue logic, NLU, and API integrations; perform root-cause analysis
• Contribute to design reviews, define quality standards, and share knowledge across the team

• 5+ years in conversational AI or dialogue systems with production exposure
• Hands-on agent building experience with multi-turn dialogues
• Strong NLU integration: intent classification, entity extraction, slot filling, dialogue state tracking
• Solid API integration skills: REST design, authentication, error handling, asynchronous responses
• Experience operating voice AI systems in production: monitoring, incident response, quality measurement, continuous improvement
• Proficiency in Python for tooling, scripting, and automation
• Experience with evaluation metrics: containment rate, task completion, NLU accuracy, overall conversation quality
• Understanding of speech pipeline fundamentals: ASR, TTS, VAD, user experience impact
• Comfort in agile environments, cross-functional collaboration, and fast learning with AI-assisted tools

• strong collaboration
• ability to translate complex dialogue design into business terms
• high-quality craftsmanship with production-focused mindset
• Python programming
• REST API design and consumption
• NLU integration (intent, entity, slot filling, dialogue state)