What are the responsibilities and job description for the Conversational AI Engineer (AI Azure Bot) position at CC Pace?
Hybrid is preferred at all campuses but open to fully remote due to the niche skillset. Locations include Pensacola, FL; Vienna, VA; San Diego, CA; Jacksonville FL; Atlanta, GA; Dallas, TX; Virginia Beach, VA.
Required Experience:
- Experience with Azure Cognitive Services - CLU (Conversational Language Understanding)
- Experience continuously improving chatbot performance via analysis to improve experience and model accuracy
- Prior experience with voice channel and transcription that is fed into a chatbot
- Collaborative and ability to work in a team environment
We are looking for a Conversational AI Engineer to design, build, and optimize AI-powered chatbots and voice assistants using Azure Bot Framework and Conversational Language Understanding (CLU). This role will focus on training, tuning, and analyzing AI models for voice-based interactions, ensuring seamless and intelligent user experiences. The primary focus of this role will be post-transcription. The voice to text side will be handled by another team.
The ideal candidate has expertise in natural language processing (NLP), Azure Cognitive Services – CLU specifically, and tuning and training a model that is receiving requests that were initiated within a voice IVR style platform.
A Conversational AI Engineer will be responsible for designing, developing, and optimizing the models within Azure Cognitive Services. Their work focuses on natural language understanding (NLU) and system integration to ensure smooth and effective human-AI interactions.
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Key Responsibilities of a Conversational AI Engineer
Conversational Model Development
- Builds and fine-tunes Conversational Language Understanding (CLU) models in platforms like Azure Language Studio.
- Trains intents, entities, and utterances for better chatbot comprehension.
- Implements context handling to maintain conversation flow across multiple turns.
Speech & Voice AI Integration (for Voice Bots)
- Integrates speech-to-text (STT) and text-to-speech (TTS) services for voice-based interactions.
Optimization & Tuning
- Continuously improves chatbot performance by analyzing user interactions and model accuracy.
Testing & Debugging
- Conducts unit testing, regression testing, and A/B testing to validate bot performance.
- Identifies and fixes misclassifications, intent overlaps, and response errors.
- Uses analytics tools to track user behavior and refine interactions.
