Engineering Journal

We built inference-audit, a reproducible Python toolkit for systematically auditing NLP dataset quality across five core dimensions. The pipeline combines deterministic checks for label distribution, missing values, near-duplicates, language contamination, and annotation consistency. Extensive testing and real-corpus evaluation exposed performance and reliability issues, leading to targeted fixes and measurable optimizations. The final system emphasizes reproducibility, explicit failure states, citable evidence, and transparent limitations.

We redesigned llm-eval-kit into a deterministic, zero-network evaluation pipeline built for reliable continuous evaluation in CI/CD environments. The new architecture introduces a decoupled criteria registry, fail-fast orchestration, hybrid semantic and symbolic verification, and graceful handling of inapplicable evaluation criteria. The implementation reached 93% test coverage across 88 tests, with cross-version CI validation from Python 3.9 to 3.12. The work demonstrates how carefully defined evaluation contracts and offline heuristics can provide reproducible, privacy-preserving model assessment without relying on external LLM-as-a-judge APIs.

Engineering faker-pk into a consistency-aware synthetic data layer where every generated field agrees with the same real-world context.

How we designed, benchmarked, and packaged ECAPA-TDNN-VHE into a pip-installable Python library with sub-second inference latency.

Engineering a 134K-sample multi-class emotion dataset and Transformer benchmark suite for low-resource South Asian NLP.

Built the core image processing, OCR, and field extraction pipeline for docling-pk, supporting Pakistani identity and education documents. Real-world testing exposed failures caused by image preprocessing assumptions and OCR layout variability, leading to more robust extraction strategies. The system now handles rotated documents, layout-dependent fields, and explicit extraction failures rather than relying on ideal document structure. The work reinforced a practical lesson: real document variability must drive OCR design, not assumptions from standard preprocessing techniques.

Engineering voiceMonitor to track acute and chronic vocal strain, estimate recovery, and adapt to each speaker's baseline.