AI Scoring And Evaluations Architect

• AI scoring architectures
• Rubric-to-scoring logic
• Scoring prompts and evaluation frameworks
• Speech, acoustic and language-analysis pipelines
• Natural language processing and machine-learning components
• Psychometric scoring and calibration logic
• Benchmark, annotation and calibration datasets
• Human-rated reference datasets
• Model accuracy, consistency and error-analysis reports
• Bias, edge-case and failure-mode analyses
• Regression-testing frameworks
• Scoring and feedback API specifications
• Scoring-model documentation and version records

Highly capable research-oriented candidate with 3–5 years.

Relevant experience could include:

• Speech recognition or speech analytics
• NLP evaluation
• Machine-learning model validation
• Automated essay or response evaluation
• LLM evaluation systems
• Acoustic or phonetic analysis
• Recommendation or classification systems

· M.Tech., M.S. or Ph.D. in Artificial Intelligence, Machine Learning, Data Science, Computer Science, Computational Linguistics, Natural Language Processing or a closely related field.

· M.Tech., M.S. or Ph.D. in Signal Processing, Electronics and Communication Engineering, Speech Technology or Audio Processing, with demonstrated experience in speech recognition, acoustic analysis, phonetics or spoken-language evaluation.

· Postgraduate or doctoral research experience in Computational Linguistics or Natural Language Processing, with substantial work in language-model evaluation, automated scoring, text analytics, human annotation or linguistic feature extraction.

· Strong applied AI researchers or machine-learning engineers with proven experience in speech, language, evaluation systems or assessment technology, even where their formal degree is in another quantitative discipline.