1. Roles & Responsibilities: 0/10  
2. Years of Experience: 0/10  
3. Education Qualification: 0/10  
4. Skills/Knowledge: 0/10  
5. Domain/Industry: 0/10  
6. Key Competencies: 0/10  
7. Certifications: 0/10  

<h5 style="font-family: Roboto; font-size: 0.88rem;"><b>Overall Summary</b></h5>  
<ol style="font-family: Roboto; font-size: 0.88rem;">
  <li>No content was extracted from the resume, making it impossible to assess the candidate’s alignment with the job requirements. As a result, all evaluation categories score 0.</li>
  <li>Without access to the resume content, we cannot determine the candidate’s experience in building and maintaining MLOps pipelines, working with GCP Vertex AI, or handling model lifecycle management.</li>
  <li>Years of experience cannot be verified due to the absence of any textual or numerical indicators in the resume.</li>
  <li>There is no information available to assess educational qualifications, such as degrees, institutions, or relevant academic background.</li>
  <li>Skills such as Python 3.10, TensorFlow, pandas, NumPy, CUDA, or any MLOps-related tools are not verifiable from the provided resume.</li>
  <li>Domain knowledge in IT or any evidence of working in a fast-paced environment with cross-functional teams is not present.</li>
  <li>No certifications related to GCP, MLOps, or machine learning frameworks are mentioned or inferable.</li>
</ol>