Senior AI Engineer

Location Name: Pune Corporate Office - Mantri

Job Purpose

We are seeking a dynamic AI/ML Engineer to join our pioneering voice Gen AI R&D team. The ideal candidate will possess a strong foundation in machine learning and a passion for innovation. This role involves developing advanced voice AI solutions.

Duties And Responsibilities

Research and Innovation: Stay abreast of the latest advancements in Gen AI/ML technologies, contributing to research initiatives and applying innovative solutions to practical problems.

Generative AI & Model Optimization

•  Fine-tune LLMs/SLMs with proprietary NBFC data.
•  Perform distillation, quantization of LLMs for edge deployment.
•  Evaluate and run LLM/SLM models on local/edge server machines.
  
  
  

Conversational Intelligence

•  Develop and fine-tune BOTs capable of negotiation using contextual understanding, emotion detection, and dynamic loan pitch logic.
•  Build intelligent Dialogue Management frameworks that adapt in real-time.
  
  
  

Speech Technology R&D

•  Evaluate Speech-to-Speech (S2S) models for natural voice responses.
•  Assess STT models for indic dialects & accuracy; explore emotion-aware TTS engines.
•  Experiment with speaker diarization for multi-speaker environments.
  
  
  

Voice Biometrics & Security

•  Collect and analyze voice samples for biometric model training.
•  Evaluate biometric algorithms for fraud prevention and authentication.
•  Implement anti-spoofing techniques to prevent deepfakes/recorded attacks.
•  Ensure data privacy compliance in voice data usage.
  
  
  

Self-Learning Frameworks

•  Build self-learning systems that adapt without full retraining (e.g., learn new rejection patterns from calls).
•  Implement lightweight local models to enable real-time learning on the edge.
  
  
  

Key Decisions / Dimensions

Model Selection & Customization

•  Choosing the right STT, TTS, and S2S models for various Indic languages and dialects.
•  Deciding between open-source vs. commercial APIs based on latency, cost, and control.
• LLM/SLM Strategy
•  Selecting appropriate LLM/SLM architectures for dialogue management and negotiation logic.
•  Deciding what to fine-tune, distill, or quantize, and what to leave generic.
• Edge vs. Cloud Architecture
•  Making trade-offs between on-device processing and cloud-based orchestration.
•  Defining what runs locally for speed/privacy and what needs backend support.
• Emotion & Dialogue Logic Integration
•  Mapping emotional cues to appropriate TTS responses and negotiation tone.
•  Designing fallback logic for unrecognized or hostile user responses.
• Voice Biometrics Algorithm Evaluation
•  Choosing and testing biometric algorithms for authentication and anti-spoofing.
•  Deciding thresholds for matching, rejection, and fraud escalation
  
  
  

Major Challenges

Building a bot that doesn't just answer but negotiates with human-like reasoning.

Running large models (LLM/STT/TTS) in low-latency, low-bandwidth environments without cloud dependency.

Understanding caller emotions in noisy, multilingual conditions (anger, hesitation, sarcasm).

Ensuring STT and TTS pipelines work well with dialect-rich, low-resource Indian languages.

Preventing fraud via recorded calls or deepfake voices.

Bot must learn from failed interactions

Required Qualifications And Experience

•  Educational Background: Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
•  Experience: 2–8 years of experience in AI/ML, with exposure to Natural Language Processing (NLP) and speech technologies.
•  Strong experience in Speech AI – STT, TTS, S2S, speaker diarization, or related areas.
•  Proficiency in LLMs/SLMs, Hugging Face, LangChain, or OpenAI stack.
•  Experience with model optimization techniques (quantization, distillation).
•  Knowledge of edge AI deployment, low-latency serving.
•  Understanding of emotion modeling, biometric systems, and anti-spoofing.
•  Experience in Python, PyTorch/TensorFlow, and scalable deployment workflows.
•  Bonus: Experience in Indian language dialects, voice data collection, or field deployments in semi-urban/rural settings.
•  LLM Finetuning, Speech AI – STT, TTS, S2S, speaker diarization