AI Engineer designing, building, and deploying AI capabilities at global media organization creating data - driven solutions. Collaborating with teams to support content personalization and audience analytics.
About the role
- Voice AI Engineering Principal overseeing the development and deployment of voice AI technologies at Zendesk. Leading a high-performing team to shape voice-enabled customer experiences.
Responsibilities
- Define the technical vision and architect the next generation of our voice-first AI Agent platform
- Own and drive complex, multi-quarter technical initiatives from concept to production
- Lead the design and development of critical, real-time voice components
- Establish and enforce engineering best practices, design patterns, and coding standards for Python-based voice agent development
- Provide technical leadership and guidance to a dedicated project team
- Actively mentor Senior and mid-level engineers
- Serve as the primary technical partner for Product Leadership, ML Science, and Infrastructure teams
- Design, establish, and continuously improve the organizational platforms and methodologies for evaluating voice agent performance and behavior
- Architect and implement advanced safety and reliability mechanisms
Requirements
- 10+ years of progressive experience in software engineering
- 4+ years focused on AI/ML applications
- 2+ years operating in a Staff, Principal, or equivalent technical leadership capacity
- Expertise in LLM-Oriented System Architecture
- Mastery in Voice AI/Spoken Dialogue Systems
- Extensive, hands-on experience building mission-critical, low-latency, streaming voice applications
- Deep proficiency with integrating and managing real-time STT/TTS models and APIs
- Advanced techniques for Voice Activity Detection (VAD) and noise suppression
- Architecting robust barge-in and interruption logic in real-time voice streams
- Deep expertise in deploying complex, large-scale AI applications to cloud platforms (AWS, GCP, or Azure)
- Proven experience optimizing LLM token budgets, latency, and cost through sophisticated model routing, caching (e.g., Redis), and quantization techniques
- Comprehensive understanding of foundational ML concepts, Retrieval-Augmented Generation (RAG) pipelines, vector databases, and advanced context management to ensure deterministic and accurate agent behavior in complex production environments
- Expert-level proficiency in Python and modern web frameworks (e.g., FastAPI, gRPC for streaming services)
Benefits
- Hybrid working arrangement
- Professional development opportunities
Job title
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