-------------------------- ---------------------------------------------- | 1.DATA SOURCES | | 2.INGEST & PREP Files |
|Kaggle resume datasets, | --> |Resumes are parsed (PDF, DOCX, TXT), |
|LinkedIn open datas, ATS | |sections detected. |
|Exports. | |OCR handles scanned docs. |
|Synthetic resume generator | │Text is normalized, tokenized, and deduped |
|for rare skills. | │Named entity recognition (NER) extracts |
----------------------------- │skills and experience. |
│Data is validated for format and diversity.│
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v---------------------- ----------------------------------| 4. MODEL TRAINING | | 3. FEATURE ENGINEERING |
| (Ranking Models) | | Skill embeddings |
| BERT-based andand | <-- | experience-level features |
| gradient-boosted | | computed using Sentence-BERT. |
| ranking trained. | | Semantic similarity profiles |
| Cross-validation | | boost ranking quality. |
---------------------- ---------------------------------- | v -------------------------------------------------------------------------------| 5. INFERENCE & RANKING PIPELINE |
| Real-time APIs score resumes with low latency against job descriptions. |
| Ranked outputs adapt dynamically. Batch scoring aids acquisition workflows. |
| Explanations highlight skill-job relevance. |
------------------------------------------------------------------------------- | v---------------------------------------------------------------------------| 6. MONITORING & FEEDBACK LOOP |
| Recruiter feedback refines matching. Hiring outcomes enrich retraining. |
| Continuous improvements enhance ranking precision and fairness. |
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