AIML Engineer – Voice (2–3 Years)
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Posted on:
10 hours ago
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1 Vacancy
Job Summary
Job Title: AI/ML Engineer Voice (23 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 complianceempowering organizations to operate with speed clarity and confidence.
We combine automation where its 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 and amp;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 and amp; 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 and amp; 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 and amp; 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 and amp; 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 and amp;D.
Security and amp; 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 and amp; telephony integration (framework-agnostic)
Realtime STT LLM 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 and amp; Engineering
Python (strong competency)
Cloud deployment understanding (AWS/Azure/GCP)
API development
Data processing and amp; pipeline creation
Secondary Skills (Good to Have)
MLOps and amp; CI/CD for ML systems
Vector databases
Prompt engineering
Model monitoring and amp; evaluation frameworks
Microservices experience
Basic UI integration understanding for voice/chat
Research reading and amp; benchmarking ability
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 to collaborate cross-functionally
Strong ownership and delivery discipline
Ability to explain complex AI concepts simply
Company Industry
IT Services and IT Consulting
Key Skills
- EIGRP
- VOIP
- VMware
- Avaya
- BGP
- LAN
- OSPF
- QoS
- Project Planning
- Scripting
- Unity
- Troubleshooting
