AI/ML Engineer Voice

Role Overview:

You are an AI Engineer Voice with expertise in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems. Your responsibilities 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. Your work involves core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. You are expected to possess strong fundamentals, the ability to prototype quickly, and contribute to R&D initiatives when necessary. Collaboration, cross-functionality, and hands-on work are essential aspects of this role.

Key Responsibilities:

• Build end-to-end voice AI systems, encompassing 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:

• Construct, deploy, and optimize ML and DL models for prediction, classification, and automation purposes.
• Train and fine-tune neural networks for text, speech, and multimodal tasks.
• Develop traditional ML systems when required (statistical, rule-based, hybrid systems).
• Perform feature engineering, model evaluation, retraining, and continuous learning cycles.

NLP, LLMs & GenAI:

• Implement NLP pipelines involving tokenization, NER, intent, embeddings, and semantic classification.
• Collaborate with LLM architectures for text + voice workflows.
• Create GenAI-based workflows and integrate models into production systems.
• Implement RAG pipelines and agent-based systems for complex automation.

Fintech & Banking AI:

• Focus on AI-driven features related to banking, financial risk, compliance automation, fraud patterns, and customer intelligence.
• Consider fintech data structures and constraints while designing AI models.

Engineering, Deployment & Collaboration:

• Deploy models on cloud or on-premises (AWS / Azure / GCP / internal infra).
• Develop robust APIs and services for voice and ML-based functionalities.
• Engage with data engineers, backend developers, and business teams to deliver complete AI solutions.
• Document systems and contribute to internal knowledge bases and R&D.

Security & Compliance:

• Adhere to fundamental AI security practices, access control, and secure data handling.
• Maintain 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.

Soft Skills:

• Clear communication and requirement understanding.
• Curiosity and research mindset.
• Self-driven problem-solving ability.
• Cross-functional collaboration.
• Strong ownership and delivery discipline.
• Ability to simplify complex AI concepts for all stakeholders. Role Overview:

You are an AI Engineer Voice with expertise in machine learning, deep learning, NLP, GenAI, and full-stack voice AI systems. Your responsibilities 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. Your work involves core ML/DL systems, voice models, predictive analytics, banking-domain AI applications, and emerging AGI-aligned frameworks. You are expected to possess strong fundamentals, the ability to prototype quickly, and contribute to R&D initiatives when necessary. Collaboration, cross-functionality, and hands-on work are essential aspects of this role.

Key Responsibilities:

• Build end-to-end voice AI systems, encompassing 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:

• Construct