AI Resume Reviewer

Document scoring with structured outputs applies to hiring, lending, and compliance. Shows rubric eval, PII handling, and bias mitigation.

A career coach with a scoring rubric on a clipboard — every point links to a specific resume line.

Scenario

Senior ML Engineer JD uploaded. Resume scores 7/10 technical fit — missing MLOps and Kubernetes. Suggests rewriting 'Built recommendation model' → 'Built rec sys serving 2M users, +12% CTR, deployed on K8s'.

Solution

In Capstone Projects, apply AI Resume Reviewer to this scenario: Senior ML Engineer JD uploaded. Identify the inputs, run the technique, validate the output, and note one thing you would monitor in production.

Do this before moving to the next module — reading alone is not enough.

Open the Code Walkthrough below and run it locally. Change one parameter related to AI Resume Reviewer (e.g. model, temperature, top_k, or tool name), observe the difference in output, and write 2–3 sentences explaining what changed.

Highlighted lines show where AI Resume Reviewer happens in the code.

1# AI Resume Reviewer — minimal example2from openai import OpenAI3
4client = OpenAI()  # create API client5
6# Ask the model to explain this topic7response = client.chat.completions.create(  # core API call for AI Resume Reviewer8    model="gpt-4o-mini",9    messages=[10        {"role": "system", "content": "You explain ai resume reviewer clearly."},11        {"role": "user", "content": f"What is ai resume reviewer?"},12    ],13    temperature=0,14)15print(response.choices[0].message.content)  # show output for debugging

• Scoring on university name prestige as proxy
• Free-text scores without rubric
• No explainability per dimension

Quick recap — the most important points from this module.