Lead – Technology Business

Job Purpose
The Lead AI role exists to drive the design, development, and implementation of advanced Gen AI/ML solutions that accelerate the organizations digital transformation. The role focuses on building scalable AI systems, leading innovation initiatives, and guiding crossfunctional teams to deliver highimpact AI capabilities aligned with business strategy. Duties and Responsibilities

Lead end-to-end development of AI/ML models and solutions.

Architect scalable AI platforms and pipelines.

Guide the team on best practices in ML engineering, MLOps, and model governance.

Collaborate with stakeholders to define AI roadmap and strategic initiatives.

Conduct research and evaluate emerging AI technologies for adoption.

Ensure responsible AI, model fairness, explainability, and compliance.

Mentor engineers and data scientists; build technical capability within the team.

Drive POCs, rapid prototyping, and conversion into production-grade solutions.

Key Decisions / Dimensions

Model Selection & Customization

Choosing the right Speech To Speech, 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 (Speech to Speech/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: Bachelors or Masters degree in Computer Science, Engineering, or a related field.

Experience: 28 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.


a)LLM Finetuning, Speech AI STT, TTS, S2S, speaker diarization