ML Case-study Interview Question: Extracting Standardized Job Roles from Ads Using Generative AI and Taxonomy.
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Case-Study question
You are a Senior Data Scientist at a large online marketplace that hosts user-generated job postings. Many job ads do not specify standardized job roles, making it hard for users to find the right listings quickly. You must create a system that automatically extracts job roles from ad titles and descriptions. You need to build a robust data pipeline that processes new and updated job posts at scale. How would you approach this challenge, considering model selection, prompt engineering, infrastructure, cost optimization, and ongoing maintenance of the taxonomy of job roles?
Detailed Solution
Overview
The objective is to design a system that extracts standardized job roles from free-text titles and descriptions in a large-scale job marketplace. The core idea is to leverage a generative AI model to parse the text and identify relevant job roles. The pipeline then integrates those extracted roles into the platform’s search index.
Data Sampling and Preprocessing
Data sampling starts by collecting a representative set of job posts from various sub-categories. This ensures coverage for roles that appear less frequently. Titles and descriptions are cleaned, truncated to a practical length (for example 200 tokens), and translated if needed. Preprocessing reduces noise and respects token limits for language model interactions.
Job-Role Taxonomy
A structured taxonomy is essential for standardized labeling. In-house subject-matter experts and data scientists analyze top-searched keywords and a sample of extracted roles. They merge them into a hierarchical taxonomy of job roles. This guides the model to output normalized categories.
Model Integration
An in-house Large Language Model (LLM) or a third-party service accepts prompts containing ad text and the known taxonomy. It returns the most relevant roles. The system saves those roles for indexing. If self-hosting a model is too costly or complex, an external managed AI Assistant can be used at first to reduce time-to-market.
Production Pipeline
A service subscribes to job ad creation and update events. Each time an ad is posted or modified, the pipeline sends the ad text to the model and receives predicted roles. Those roles are forwarded to the search team’s indexing system. This ensures that incoming user queries can match by job role. A one-time backfill is used to label older ads.
Cost Management
Frequent queries to an external AI service can be costly. Many solutions rely on a pay-as-you-go model or monthly usage tiers. If usage volume increases, a specialized self-hosted model can become more economical. Doing an early proof of concept with a managed service clarifies potential performance and helps estimate full-scale costs.
Maintenance and Updates
Job categories evolve, so the taxonomy must stay current. Any category addition or modification triggers an update to the role taxonomy. A new job-role extraction run might be necessary to ensure consistency. An automated detection system for category changes is valuable to keep results accurate.
Example Python Snippet
import requests
def extract_roles(ad_text, category, api_endpoint, api_key):
payload = {
"text": ad_text,
"category": category
}
headers = {
"Authorization": f"Bearer {api_key}"
}
response = requests.post(api_endpoint, json=payload, headers=headers)
return response.json().get("extracted_roles", [])
# Usage
sample_text = "We are looking for a skilled gardener to maintain a local greenhouse."
roles = extract_roles(sample_text, "Agriculture_and_Gardening", "https://model-api/v1/extract", "my_api_key")
print(roles)
This code snippet shows a simple function that posts job ad text and category information to a role-extraction API.
Follow-up question 1
How would you handle ads that contain multiple potential job roles in a single listing?
Answer and Explanation
Many ads describe multiple tasks. If the model returns multiple valid roles, the pipeline can store all of them, tagged as secondary or primary based on text frequency or semantic relevance. One approach is to instruct the LLM to highlight the most central role first, then list any additional roles. The indexing system supports multi-role tags to ensure that even partial matches surface in user searches.
Follow-up question 2
How would you prevent or handle hallucinations by a generative AI model that might output job roles not present in the ad text?
Answer and Explanation
A role extraction prompt can emphasize the need to output only roles directly stated or implied in the text. Additional guardrail checks include verifying each extracted role against known keywords or patterns. If the result does not align with the text, the system can discard it or flag it for human review. A final rule-based filter might check that the text contains terms indicating a specific role before trusting the model’s output.
Follow-up question 3
How would you incorporate search ranking adjustments for standardized job roles?
Answer and Explanation
The existing ranking mechanism might be updated so that exact matches in standardized roles get higher priority. If a user searches for “gardener,” listings with the “gardener” role are surfaced first. The final rank still considers other factors like location and relevance. An A/B test can compare the new approach vs the old approach. The system tracks search extensions (like when a user modifies their query) and successful events (like a user clicking on or applying to a listing). Improvement in these metrics indicates the success of the role-based ranking.
Follow-up question 4
What strategies would you use to ensure the system adapts to changes in the taxonomy?
Answer and Explanation
A change-detection step tracks modifications in the category structure. This triggers a partial or full regeneration of the role taxonomy. The pipeline re-analyzes sample ads for the newly created or modified categories and updates the LLM prompts. Stored roles in the search index may need re-verification to prevent inconsistencies. This process requires minimal downtime because the system handles old and new taxonomies in parallel, preserving stable search results for users.
Follow-up question 5
How would you decide whether to continue using a managed AI service or transition to a self-hosted language model?
Answer and Explanation
The choice depends on costs, latency requirements, and data governance. If usage volume grows large, hosting an open-source model fine-tuned for job-role extraction becomes more affordable. A self-hosted setup also enables deeper customizations and direct control over data. It requires infrastructure, teams with ML deployment skills, and additional engineering. A pilot project with the managed service clarifies performance, cost, and user impact metrics. A break-even analysis reveals when self-hosting might become cost-effective.