Senior AI Engineer (Voice Systems)

Senior AI Exprt - Voice Bot Systems

• ·      Work Location: 

o  Hanoi: TechnoPark Tower, Vinhomes Ocean Park, Hanoi 

o  Hochiminh: Vincom Dong Khoi, HCM 

Job overview 

We are looking for an AI Expert to join our team as the technical anchor for next-generation real-time voice bot systems. While our Software Engineers focus on integration and infrastructure, this role owns the intelligence layer — designing, training, evaluating, and optimizing the AI models that make voice interactions actually work: from acoustic understanding to natural speech synthesis, speaker identification, and language comprehension in noisy, real-world conditions.

The ideal candidate has built voice AI systems in production — not just integrated APIs, but understood what's happening inside them and made deliberate choices about model architecture, training data, evaluation methodology, and latency trade-offs. You will work directly with Software Engineers to move models from research into production and with product stakeholders to define what "good" means for a voice bot.

Key Responsibilities: 

Voice AI Research & Development

• Design and develop end-to-end voice pipelines: ASR → NLU → dialogue management → TTS, with full understanding of the trade-offs at each stage

• Research, evaluate, and fine-tune models for Vietnamese-language voice tasks: ASR accuracy on domain-specific vocabulary (vehicle names, locations, plate numbers), TTS naturalness, speaker diarization in multi-party calls

• Build and maintain evaluation frameworks for voice AI quality: Word Error Rate (WER), Real-Time Factor (RTF), MOS scoring, speaker turn accuracy, end-to-end latency benchmarks

Model Fine-tuning & Training

• Fine-tune ASR models (Whisper, Gipformer, wav2vec 2.0) on domain-specific and accent-specific Vietnamese data

• Fine-tune TTS models (F5-TTS, VoxCPM2, VITS) to produce natural, brand-consistent voice output for CSKH scenarios

• Build and curate training datasets: audio collection pipelines, annotation tooling, data quality validation

• Apply speaker diarization and voice activity detection (VAD) to segment and attribute multi-speaker call audio

LLM & NLU Integration

• Design prompt strategies and context management for LLM-driven dialogue in voice-specific constraints (no markdown, short sentences, natural prosody cues)

• Develop intent classification, entity extraction, and slot-filling components tuned for spoken Vietnamese, including noisy transcripts from imperfect ASR

• Evaluate and mitigate hallucination in voice LLM pipelines, including ASR-grounded prompting and transcript correction techniques

Production & Optimization

• Optimize inference latency for real-time constraints: quantization, batching, streaming inference, model distillation

• Design and run A/B experiments on model variants in production; define statistical evaluation criteria for voice quality metrics

• Collaborate with Software Engineers on model serving architecture (GPU inference servers, ONNX runtime, TorchServe, Triton)

• Build monitoring pipelines for model performance drift, ASR degradation, and TTS quality regression in production

Qualifications:  

Education

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, Applied Mathematics, Linguistics, or a directly related field. PhD is a plus for research-heavy responsibilities.

Hands-on voice AI experience (required)

• 3–5+ years of hands-on experience building and deploying voice AI systems — not just integrating cloud APIs, but working with the models themselves

• Demonstrated experience with at least two of: ASR fine-tuning, TTS model training, speaker diarization, VAD, voice conversion, or acoustic modeling

• Prior production experience with real-time voice pipelines where latency, reliability, and audio quality were hard constraints

Deep learning & NLP foundations

• Solid understanding of sequence modeling (Transformer, CTC, RNN), attention mechanisms, and how they apply to speech and language tasks

• Familiarity with acoustic features (MFCCs, mel spectrograms, filterbanks) and audio signal processing fundamentals

• Experience with speech-specific deep learning frameworks: ESPnet, SpeechBrain, NeMo, Hugging Face transformers + datasets for audio

Engineering skills

• Strong Python; comfortable with PyTorch or TensorFlow for model development and fine-tuning

• Ability to write production-quality code, not just research scripts: modular, testable, observable

• Experience with ML experiment tracking (MLflow, W&B), model versioning, and reproducibility practices

• Familiarity with Docker and GPU infrastructure for model serving

Evaluation mindset

• Knows that a model is only as good as its evaluation — can design rigorous offline and online evaluation protocols for voice AI

• Experience with human evaluation studies (MOS, MUSHRA, preference tests) and automated proxies

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Nice to Have

• Experience with multilingual or low-resource language ASR/TTS, especially Vietnamese

• Familiarity with speaker diarization systems (pyannote, NeMo MSDD) applied to call center audio

• Knowledge of telephony audio constraints: G.711 µ-law/a-law codecs, 8kHz narrowband limitations, packet loss concealment

• Experience with streaming ASR (chunk-based inference, endpointing) for real-time applications

• Contributions to open-source speech/NLP projects or published research in voice AI

What This Role Is Not

• This is not an API integration role. We need someone who understands what happens inside the model, not just how to call it.

• This is not a pure research role. Models need to ship into production voice systems with real latency and reliability requirements.

• This is not a data annotation role. You will design the data strategy and evaluation criteria — others will execute annotation at scale.

What We Offer

• Ownership of the AI layer in a production voice system serving real customers

• Direct collaboration with Software Engineers, DevOps, and product teams — short feedback loops between research and production

• Access to real call audio data for training and evaluation (with appropriate privacy controls)

• Competitive compensation with equity