Senior Speech AI Engineer (Human-Realistic Voices, Multilingual STT and TTS) Company: INTIME IT Services (INTIME Voice AI)

Location:
Remote. At least 4 hours of overlap with IST. Engagement: Full time or part time (minimum 20 hours per week)

Experience:
5+ years in software or ML engineering, including 2+ years in production speech AI (ASR, TTS, or voice AI) Compensation: To be discussed, based on experience and engagement type (full-time or part-time)

About us
INTIME IT Services is an 11-year-old SAP and AI consulting company with operations in Kolkata, Dhaka, and Dubai. INTIME Voice AI is our real-time voice agent platform, live with paying clients. We are expanding across India, Bangladesh, Thailand, and the UAE. Our callers speak national languages, regional languages, and mixed-language speech, often on noisy phone lines. The role You make our agents understand every caller and sound indistinguishable from a human, in every main and regional language of our markets. Our benchmark is the realism of Fish Audio-class voice models: natural prosody, emotion, breathing, and conversational rhythm. Language scope India: Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia, Assamese, Indian English, and code-switched Hinglish Bangladesh: Bangla (standard) and regional dialects such as Sylheti and Chittagonian, plus code-switched Bangla-English Thailand: Central Thai and regional varieties such as Isan, Northern Thai, and Southern Thai, plus Thai-English code-switching UAE: Gulf and Emirati Arabic, Modern Standard Arabic, English, and the main expatriate languages (Hindi, Urdu, Malayalam, Tagalog) First launches: Bengali (India and Bangladesh), Hindi, Hinglish, Indian English, Gulf Arabic, and Thai. Regional languages follow in priority order set by client demand. What you will do Multilingual STT Build evaluation sets from real 8 kHz telephony audio and 16 kHz web audio for each language and dialect Benchmark 3 or more STT providers and open-source models per language on WER and CER, latency, streaming partial stability, and cost Define per-language routing rules for the model routing layer Fine-tune open-source ASR (Whisper-family, Conformer or RNN-T models) on domain and dialect data where commercial providers underperform Handle code-switching, accents, noise, echo,



and low-bitrate phone audio Build contextual biasing and hotword boosting for names, places, brands, and domain terms Human-realistic TTS Benchmark 3 or more TTS providers and open-source models per language on naturalness, speaker similarity, latency, and cost Deploy and fine-tune neural codec language model TTS and flow-matching TTS (Fish Speech/OpenAudio, CosyVoice, F5-TTS, StyleTTS 2, XTTS, or similar) for languages where commercial voices fall short Deliver expressive control: emotion, speaking style, emphasis, speaking rate, pitch, and pause control Generate paralinguistic features where appropriate: breaths, short fillers, laughter, and natural hesitation Streaming TTS with time to first byte at or below 150 ms and no audible artifacts at chunk boundaries Text normalization per language: numbers, dates, currency, abbreviations, and mixed scripts Pronunciation lexicons and G2P handling for each language Voice creation (authorized cloning) Zero-shot and few-shot cloning from 10 to 60 seconds of reference audio, plus fine-tuned voices from larger datasets for premium clients Cross-lingual voice transfer: the same voice speaking multiple languages A consent pipeline: signed authorization, stored consent evidence, speaker verification of the voice owner, revocation, and deletion Anti-misuse controls: no cloning of public figures or unauthorized voices, plus audio watermarking where available Conversational realism Semantic end-of-turn detection tuned per language, since pause patterns differ across languages Backchannels and acknowledgments timed to the callers speech Natural barge-in behavior: stop, listen, and respond to the interruption Filler phrases tied to the real system state. The agent never says let me check” unless a tool is running.



Evaluation Blind MOS and CMOS listening tests with native speakers per language Speaker similarity scored by speaker-embedding cosine similarity (ECAPA-TDNN or WavLM-based) TTS intelligibility measured by ASR round-trip WER A release gate that blocks any release that drops WER, MOS, or similarity You must have done this before Shipped STT or TTS models or improvements to a production voice product with real users Fine-tuned and deployed an ASR or TTS model on custom data Built speech evaluation sets and run WER and MOS evaluations Worked with at least one low-resource, regional, or code-switched language Integrated 3 or more STT and 3 or more TTS systems, commercial or open-source Built or deployed voice cloning in production Technical requirements Strong Python and PyTorch. torchaudio, librosa, and ESPnet, NeMo, or Hugging Face speech tooling. Telephony audio: 8 kHz and 16 kHz, mu-law and A-law, Opus, resampling VAD and turn-detection models GPU training and inference, including mixed precision and quantization for low-latency serving Strong plus Native or fluent in one or more of: Bengali, Hindi, Thai, Arabic, Tamil, Telugu Arabic dialect or Thai speech work Speech-to-speech or full-duplex models Published research or open-source speech contributions Outcomes expected in 90 days WER and MOS baselines for all first-launch languages on real call audio Bengali (India and Bangladesh), Hindi, Hinglish, Indian English, Gulf Arabic, and Thai in production, using the best-performing STT and TTS per language At least 2 regional languages production-ready TTS naturalness of MOS 4.3 or higher in blind native-speaker tests for first-launch languages At least 3 authorized custom voices live, with speaker similarity of 0.80 or higher and full consent records Streaming TTS time to first byte at or below 150 ms Please do not apply if You have less than 2 years of production speech AI experience Your speech work is limited to academic projects or tutorials You have not deployed a speech model to production

How to apply
Send your CV to [email protected] with the subject “Senior Speech AI Engineer, Full-time” or “Part-time”. Include: The languages and dialects you have shipped One STT or TTS improvement you delivered, with before and after WER or MOS Samples or links to voices or models you built, if shareable Hours per week available, if applying part-time

📌 Senior Speech AI Engineer (Kolkata)
🏢 Intime It Services
📍 Kolkata