About the Role

As Lead Voice AI QA, you will use your computational-linguistics expertise to build and scale Navana.ai’s QA team for our ASR and TTS systems covering 10+ Indic languages. You will:

• Recruit, train and mentor native-language experts, linguists and QA specialists.
• Oversee data-processing tasks such as text sanitisation, audio segmentation, audio transcription, speaker labeling, intent–entity labeling, TTS quality assessment and ASR error analysis (misrecognitions, background noise, accent/dialect robustness).
• Create benchmarking pipelines that provide a relative measure of performance of AI models on customer specific dataset.
• Define data requirements, publish guideline documents and quality standards, and manage external data vendors and budgets to secure high-quality speech and text corpora.
• Design, implement and manage data-versioning pipelines, large-scale error-analysis pipelines and automated QA processes; with clear evaluation metrics; to uncover, diagnose and quantify model errors, driving continuous improvements.
• Partner with AI scientists, engineers, product managers and enterprise BFSI clients to turn analytical insights into actionable interventions (e.g., curated LM data, keyword boosts, ASR fine-tuning strategies for AI team) and ensure every deployment exceeds quality expectations.

Key Responsibilities

• Data Specification, QA Team & Vendor Management
  • Define Data & Quality Standards – Set detailed data requirements for training, fine-tuning, and evaluating ASR and TTS models in 10 + Indic languages; publish clear guidelines.
  • Run Internal QA Operations – Shape workflows for the in-house QA team (task assignment, spot checks, audits, feedback loops) to ensure every annotation, transcription, and evaluation meets or exceeds the defined standards.
  • Collaborate with External Vendors – Specify deliverables, develop collection processes, and enforce compliance with quality targets for outsourced data work.
  • Draft Protocols & Scopes – Write precise data-collection protocols and scope-of-work documents that cover both internal QA tasks and vendor contributions.
  • Own Timelines & Delivery – Set schedules and quality gates, track progress of both in-house and vendor pipelines, and resolve blockers to guarantee on-time delivery of high-quality speech and text corpora.
• Error-Analysis Pipeline Development
  • Design and manage large-scale error-analysis pipelines grounded in linguistic insight and deep ASR/TTS knowledge.
  • Build LLM-powered analysis tools: create prompt-engineering workflows and custom LLM utilities that automatically identify errors, surface root causes, and generate diagnostic reports.
  • Develop robust methodologies to uncover, diagnose, and categorize model errors (e.g., mis-recognitions, prosody failures, hallucinations).
  • Present error-analysis insights and recommendations to product managers, speech scientists and enterprise BFSI stakeholders.
• Automated QA & Evaluation
  • Create and refine automated quality checks, verifying annotations and automatically spotting errors, to accelerate data throughput while maintaining stringent standards.
  • Maintain evaluation datasets and benchmarking frameworks for ASR, TTS and LLMs.
  • Conduct acoustic-phonetic and conversational analyses on production data to identify patterns, anomalies, and improvement opportunities.

Qualifications & Experience

• Master’s or Ph.D. in Computational Linguistics, Linguistics or a related field, plus a strong Computer-Science foundation.
• 2+ years evaluating ASR/TTS models, building evaluation frameworks, curating data and managing vendor partnerships—preferably with Indic languages or other low-resource settings.