Job Title: AI/ML Engineer – Voice (2–3 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 compliance—empowering 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 ↔ 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 & 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

2–3 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.

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