Voice Bot Systems Project
About the Role
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.
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
Required 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
Kindly send your CV via: [protected info]