AI/ML Engineer – Voice (2–3 Years)

Job Title: AI/ML Engineer â Voice (2â3 Years) Location: Bengaluru (On-site) Employment Type: Full-time About Impacto Digifin Technologies Impacto Digifin Technologies enables enterprises to adopt digital transformation through intelligent, AI-powered solutions. Our platforms reduce manual work, improve accuracy, automate complex workflows, and ensure complianceâempowering organizations to operate with speed, clarity, and confidence. We combine automation where it's fastest with human oversight where it matters most. This hybrid approach ensures trust, reliability, and measurable efficiency across fintech and enterprise operations. Role Overview We are looking for an AI Engineer Voice with strong applied experience in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems . This role requires someone who can design, build, deploy, and optimize end-to-end voice AI pipelines , including speech-to-text, text-to-speech, real-time streaming voice interactions, voice-enabled AI applications, and voice-to-LLM integrations. You will work across core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. The ideal candidate is an applied engineer with strong fundamentals, the ability to prototype quickly, and the maturity to contribute to R&D when needed. This role is collaborative, cross-functional, and hands-on. Key Responsibilities Voice AI Engineering 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. Machine Learning & Deep Learning 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. Build traditional ML systems where needed (statistical, rule-based, hybrid systems). Perform feature engineering, model evaluation, retraining, and continuous learning cycles. NLP, LLMs & GenAI Implement NLP pipelines including tokenization, NER, intent, embeddings, and semantic classification. Work with LLM architectures for text + voice workflows. Build GenAI-based workflows and integrate models into production systems. Implement RAG pipelines and agent-based systems for complex automation. Fintech & Banking AI Work on AI-driven features related to banking, financial risk, compliance automation, fraud patterns, and customer intelligence. Understand fintech data structures and constraints while designing AI models. Engineering, Deployment & Collaboration 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. Document systems and contribute to internal knowledge bases and R&D. Security & Compliance Follow fundamental best practices for AI security, access control, and safe data handling. Awareness of financial compliance standards (plus, not mandatory). Follow internal guidelines on PII, audio data, and model privacy. Primary Skills (Must-Have) Core AI Machine Learning fundamentals Deep Learning architectures NLP pipelines and transformers LLM usage and integration GenAI development Voice AI (STT, TTS, VAD, real-time pipelines) Audio processing fundamentals Model building, tuning, and retraining RAG systems AI Agents (orchestration, multi-step reasoning) Voice Engineering End-to-end voice application development Voice calling & telephony integration (framework-agnostic) Realtime STT â LLM â TTS interactive flows Voice chat system development Voice-to-AI model integration for automation Fintech/Banking Awareness High-level understanding of fintech and banking AI use cases Data patterns in core banking analytics (advantageous) Programming & Engineering Python (strong competency) Cloud deployment understanding (AWS/Azure/GCP) API development Data processing & pipeline creation Secondary Skills (Good To Have) MLOps & CI/CD for ML systems Vector databases Prompt engineering Model monitoring & evaluation frameworks Microservices experience Basic UI integration understanding for voice/chat Research reading & benchmarking ability Qualifications 2â3 years of practical experience in AI/ML/DL engineering. Bachelor's/Master's degree in CS, AI, Data Science, or related fields. Proven hands-on experience building ML/DL/voice pipelines. Soft Skills Experience in fintech or data-intensive domains preferred. Clear communication and requirement understanding Curiosity and research mindset Self-driven problem solving Ability to collaborate cross-functionally Strong ownership and delivery discipline Ability to explain complex AI concepts simply

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