AI Engineer – Text-to-Speech

Overview
We’re building an AI Agent that can engage in real-time, human-like conversations with prospects — enabling businesses to automate outreach and scale communication with intelligence and empathy.

We’re now looking for an AI Engineer who can help us push the boundaries of speech intelligence, focusing on real-time TTS, STT, and conversational voice models.

Responsibilities

• Design, build, and optimize data pipelines for speech-based AI systems.
• Develop and fine-tune Text-to-Speech (TTS) and Speech-to-Text (STT) models, including Whisper fine-tuning.
• Implement Voice Activity Detection (VAD) for real-time audio processing.
• Apply Natural Language Processing (NLP) techniques for intent detection, dialogue flow, and response generation.
• Collaborate on integrating speech + NLP models into scalable, real-time conversation pipelines.
• Collaborate with product and backend teams to deploy scalable architectures for voice-based agents.
• Research and experiment with state-of-the-art voice synthesis, speaker adaptation, and emotion modeling.
• Continuously improve the latency, clarity, and realism of AI-generated speech.

Requirements

• Solid experience with Machine Learning / Deep Learning in Speech or NLP domains.
• Proficiency in PyTorch / TensorFlow and libraries such as Hugging Face Transformers.
• Practical experience with TTS, STT, or NLP models (e.g., Whisper, Tacotron, FastSpeech, GPT, BERT, LLaMA).
• Understanding of data engineering and real-time inference pipelines.
• Familiarity with cloud deployment (AWS/GCP/Azure) and API integration.
• Strong analytical thinking, communication, and ownership mindset.

Job Type: Full-time

Application Question(s):

• Have you worked with Text-to-Speech (TTS) or Speech-to-Text (STT) models such as Whisper?
• Do you have hands-on experience with NLP or LLM models (e.g., BERT, GPT, or Hugging Face Transformers)?
• Have you ever fine-tuned a pre-trained AI model for speech or text applications?
• How comfortable are you with building data pipelines or real-time AI architectures?
• Share one AI or NLP project you’ve worked on and the tools or frameworks you used.

Work Location: Remote