CogniTensor is seeking a talented Data Scientist with strong hands-on experience in Voicebot development, Conversational AI, and Machine Learning**. The ideal candidate should have a solid foundation in NLP, speech technologies, and modern AI frameworks capable of building end-to-end voice-driven automation systems for finance, banking, and insurance use cases.
You will collaborate with cross-functional teams to design, build, and deploy **AI-powered voicebots, chatbots, and predictive models that enhance customer engagement and operational efficiency.
Responsibilities
- Develop and deploy machine learning, NLP, and speech-based models for BFSI datasets.
- Build end-to-end Voicebots covering ASR, NLU, dialogue management, and TTS pipelines.
- Work with speech recognition engines (Whisper, Google ASR, Azure Cognitive Speech, Amazon Transcribe) and text-to-speech systems(Google WaveNet, Amazon Polly, Azure TTS).
- Create and optimise Chatbots using Rasa, Dialogflow, LangChain, or LLM-based frameworks.
- Implement entity extraction, intent classification, embeddings, and transformer-based models.
- Perform data preprocessing, feature engineering, and model optimisation for large-scale datasets.
- Integrate AI models into production environments with engineering teams using REST APIs, Docker, and cloud services.
- Analyse model performance, improve accuracy, and ensure low-latency speech/voice response systems.
- Collaborate on the design of conversational flows, voice UX, and call automation logic.
Requirements
- Bachelor's or Master's degree in Computer Science, Data Science, or related fields.
- Strong proficiency in Python, Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch.
- Hands-on experience with ASR / TTS technologies: OpenAI Whisper, Google Speech-to-Text, Azure Cognitive Speech, Amazon Transcribe, TTS engines like WaveNet, Amazon Polly, Azure Neural TTS
- Expertise in NLP, NLU, and Conversational AI frameworks such as Rasa, Dialogflow, LangChain, and uggingFace Transformers.
- Experience in building Voicebots, Chatbots, and conversational flows.
- Familiarity with BFSI datasets and domain-specific modelling challenges.
- Cloud experience with AWS / Azure / GCP for model deployment (Lambda, EC2 S3 Cloud Functions).
- Strong problem-solving skills and ability to communicate technical insights clearly.
- Knowledge of Twilio Voice, SIP/VoIP systems, or call automation platforms.
- Experience with real-time streaming pipelines for speech processing.
- Exposure to vector databases (FAISS, Pinecone) for RAG-based chatbots.
- Experience using Docker, CI/CD pipelines, and microservices architecture.
- Proven experience in building and deploying Voicebots (ASR + NLU + TTS end-to-end).
- Practical experience creating Chatbots for customer support or automation.
- Experience in AI implementation within financial services.
- Cloud-based ML deployment exposure (AWS, Azure, GCP).
This job was posted by Pooja Kumari from CogniTensor.
