Automated Candidate Screening Against Complex Criteria

Filter the entire set of 30 CVs to identify all candidates who meet the minimum requirement of 4+ years of professional experience AND explicitly demonstrate proficiency in at least two of the following core areas: Deep Learning (e.g., CNN, RNN, YOLO), Big Data Frameworks (e.g., PySpark, Hadoop, Hive), or MLOps/Deployment (e.g., Kubeflow, Docker, Flask). List the names of all candidates who pass this initial filter.
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Completed
📄 Relevant document found: 30 files
🔍 Analyzing files: cv (1).pdf, cv (2).pdf, cv (3).pdf, cv (4).pdf, cv (5).pdf
🔍 Analyzing files: cv (6).pdf, cv (7).pdf, cv (8).pdf, cv (9).pdf, cv (10).pdf
🔍 Analyzing files: cv (11).pdf, cv (12).pdf, cv (13).pdf, cv (14).pdf, cv (15).pdf
🔍 Analyzing files: cv (16).pdf, cv (17).pdf, cv (18).pdf, cv (19).pdf, cv (20).pdf
🔍 Analyzing files: cv (21).pdf, cv (22).pdf, cv (23).pdf, cv (24).pdf, cv (25).pdf
🔍 Analyzing files: cv (26).pdf, cv (27).pdf, cv (28).pdf, cv (29).pdf, cv (30).pdf
✅ Finished analyzing: cv (11).pdf, cv (12).pdf, cv (13).pdf, cv (14).pdf, cv (15).pdf
✅ Finished analyzing: cv (1).pdf, cv (2).pdf, cv (3).pdf, cv (4).pdf, cv (5).pdf
✅ Finished analyzing: cv (21).pdf, cv (22).pdf, cv (23).pdf, cv (24).pdf, cv (25).pdf
✅ Finished analyzing: cv (16).pdf, cv (17).pdf, cv (18).pdf, cv (19).pdf, cv (20).pdf
✅ Finished analyzing: cv (26).pdf, cv (27).pdf, cv (28).pdf, cv (29).pdf, cv (30).pdf
✅ Finished analyzing: cv (6).pdf, cv (7).pdf, cv (8).pdf, cv (9).pdf, cv (10).pdf
✅ Analysis complete: 30 files processed
Analyze_Project_Files
Completed
Based on the comprehensive analysis of all 30 CVs against the specified minimum requirements, only two candidates successfully passed the initial filter. The filtering criteria were: 1. **Minimum Professional Experience:** 4+ years. 2. **Technical Proficiency:** Explicit proficiency in at least two of the following core areas: * Deep Learning (DL): (e.g., CNN, RNN, YOLO) * Big Data Frameworks (BD): (e.g., PySpark, Hadoop, Hive) * MLOps/Deployment (MLOps): (e.g., Kubeflow, Docker, Flask) ### Candidates Who Pass the Initial Filter The following two candidates meet both the minimum experience and the dual technical proficiency requirements: 1. **MATILDA BENHAMS** 2. **Macy Williams** --- ### Detailed Filtering Breakdown The analysis of the 30 CVs revealed that most candidates either lacked the minimum 4 years of professional experience or, more commonly, possessed the required experience but lacked explicit proficiency in at least two of the specified core technical areas (Deep Learning, Big Data Frameworks, or MLOps/Deployment). The table below summarizes how the two successful candidates met the criteria: | Candidate Name | Total Professional Experience | Core Area 1 | Core Area 2 | Core Area 3 | Core Area Count | Source File | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | **MATILDA BENHAMS** | ~4.9 years | Deep Learning modelling | N/A | Flask (MLOps) | 2 | cv (11).pdf | | **Macy Williams** | ~5.4 years | Deep Learning (YOLO, CNN) | PySpark (Big Data) | Kubeflow (MLOps) | 3 | cv (1).pdf | ### Summary of Other Candidates' Failure Points The remaining 28 candidates failed the filter for the following reasons: | Failure Category | Example Candidates | Reason for Failure | | :--- | :--- | :--- | | **Experience & Skills Gap** | DIVYANK SINGH, MD NEYAZ, MOHD. RASHID | Failed both the 4+ years experience and the 2+ core areas requirement. | | **Skills Deficit** | MIRAD YASTEIN, Nolan Drucks, Lehna Patrick | Met the 4+ years experience requirement, but only demonstrated proficiency in **one** core area (e.g., Deep Learning or Big Data, but not both). | | **Non-Relevant Skills** | Farman Ali, Nagaraj.P, CHANDAN GAUR | Met the 4+ years experience requirement, but skills were in non-core areas like Electrical Engineering, Industrial Automation (PLC/SCADA), or Mechanical CAD. | | **Experience Deficit** | Keisuke Yamamoto, Jaroslav Chechnik, ATUL SINGH | Demonstrated proficiency in 1 or 2 core areas, but lacked the minimum 4+ years of professional experience. | Sources: - cv (11).pdf - cv (1).pdf - cv (12).pdf - cv (4).pdf - cv (9).pdf - cv (10).pdf - cv (16).pdf - cv (28).pdf

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