Pratham International

AI Engineer

• Lead development and deployment of speech recognition systems for automated reading assessment across multiple languages
• Work across the full ASR pipeline, from data collection, annotation, data preparation, and model training to decoding, alignment, error analysis, and deployment
• Provide technical inputs to speech data collection design across languages, dialects, age groups, reading levels, environments, devices, and target items
• Define annotation guidelines and quality checks for transcripts, reading errors, disfluencies, background speech, and related phenomena
• Identify data gaps and annotation inconsistencies through model error analysis
• Recommend targeted data collection or re-annotation
• Design annotation schemas and inter-annotator quality processes
• Train, fine-tune, and evaluate ASR models for children’s read speech across languages and accents
• Build pipelines for audio preprocessing, segmentation, normalization, transcription alignment, and model training
• Adapt ASR architectures such as Whisper, wav2vec 2.0, Conformer, CTC/RNN-T, or similar models
• Optimize models for low-bandwidth, store-and-forward, server-based, and potentially offline/mobile inference
• Detect substitutions, omissions, insertions, repetitions, partial-word attempts, hesitations, and self-corrections
• Implement forced alignment, phoneme-level alignment, or pronunciation-scoring approaches where required
• Conduct systematic error analysis by language, reading level, age, accent, gender, recording conditions, and device
• Collaborate with assessment experts and field teams to translate model outputs into reading accuracy, WCPM, fluency, and reading-level measures
• Define and track ASR metrics including WER/CER, word-level accuracy, error-detection precision/recall, alignment accuracy, and reading-score agreement with human evaluators
• Build reproducible experimentation and model-evaluation pipelines
• Maintain clear technical documentation

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