AIML Engineer Voice

Role Overview: You are expected to be an AI Engineer Voice with expertise in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems. Your responsibilities will include designing, building, deploying, and optimizing end-to-end voice AI pipelines, such as speech-to-text, text-to-speech, real-time streaming voice interactions, voice-enabled AI applications, and voice-to-LLM integrations. You will be working on core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. This role is collaborative, cross-functional, and hands-on. Key Responsibilities: - Build end-to-end voice AI systems including STT, TTS, VAD, audio processing, and conversational voice pipelines. - Implement real-time voice pipelines involving streaming interactions with LLMs and AI agents. - Design and integrate voice calling workflows, bi-directional audio streaming, and voice-based user interactions. - Develop voice-enabled applications, voice chat systems, and voice-to-AI integrations for enterprise workflows. - Build and optimize audio preprocessing layers (noise reduction, segmentation, normalization). - Implement voice understanding modules, speech intent extraction, and context tracking. - Build, deploy, and optimize ML and DL models for prediction, classification, and automation use cases. - Train and fine-tune neural networks for text, speech, and multimodal tasks. - Implement NLP pipelines including tokenization, NER, intent embeddings, and semantic classification. - Work on AI-driven features related to banking, financial risk, compliance, automation, fraud patterns, and customer intelligence. - Deploy models on cloud or on-prem (AWS/Azure/GCP/internal infra). - Build robust APIs and services for voice and ML-based functionalities. - Collaborate with data engineers, backend developers, and business teams to deliver end-to-end AI solutions. - Follow fundamental best practices for AI security, access control, and safe data handling. - Awareness of financial compliance standards. - Follow internal guidelines on PII, audio data, and model privacy. Qualifications: - 23 years of practical experience in AI/ML/DL engineering. - Bachelors/Masters degree in CS, AI, Data Science, or related fields. - Proven hands-on experience building ML/DL/voice pipelines. - Experience in fintech or data-intensive domains preferred. Additional Details: Impacto Digifin Technologies enables enterprises to adopt digital transformation through intelligent AI-powered solutions. The platforms offered by Impacto Digifin Technologies aim to reduce manual work, improve accuracy, automate complex workflows, and ensure compliance, empowering organizations to operate with speed, clarity, and confidence. The company follows a hybrid approach combining automation with human oversight to ensure trust, reliability, and efficiency across fintech and enterprise operations. Role Overview: You are expected to be an AI Engineer Voice with expertise in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems. Your responsibilities will include designing, building, deploying, and optimizing end-to-end voice AI pipelines, such as speech-to-text, text-to-speech, real-time streaming voice interactions, voice-enabled AI applications, and voice-to-LLM integrations. You will be working on core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. This role is collaborative, cross-functional, and hands-on. Key Responsibilities: - Build end-to-end voice AI systems including STT, TTS, VAD, audio processing, and conversational voice pipelines. - Implement real-time voice pipelines involving streaming interactions with LLMs and AI agents. - Design and integrate voice calling workflows, bi-directional audio streaming, and voice-based user interactions. - Develop voice-enabled applications, voice chat systems, and voice-to-AI integrations for enterprise workflows. - Build and optimize audio preprocessing layers (noise reduction, segmentation, normalization). - Implement voice understanding modules, speech intent extraction, and context tracking. - Build, deploy, and optimize ML and DL models for prediction, classification, and automation use cases. - Train and fine-tune neural networks for text, speech, and multimodal tasks. - Implement NLP pipelines including tokenization, NER, intent embeddings, and semantic classification. - Work on AI-driven features related to banking, financial risk, compliance, automation, fraud patterns, and customer intelligence. - Deploy models on cloud or on-prem (AWS/Azure/GCP/internal infra). - Build robust APIs and services for voice and ML-based functionalities. - Collaborate with data engineers, backend developers, and business teams to deliver end-to-end AI solutions. - Follow fundamental best practices for AI security, access control, and safe data handling. - Awareness of financial co