RAG and scikit-learn frequently appear together in AI job postings, with 194 current openings requiring both skills. The most common role for this combination is AI Product Manager. Jobs requiring both skills pay up to $238K on average. Below you'll find detailed salary data, hiring companies, and related skill combinations.
RAG and scikit-learn appear together in 194 current AI job postings, most commonly for AI Product Manager positions. This combination signals that employers are looking for versatile engineers who can work across multiple layers of the AI stack. Companies hiring for this pair tend to value candidates who can bridge different technical domains rather than specialize in just one.
Top Roles Requiring RAG + scikit-learn
AI Product Manager60 jobs
AI/ML Engineer52 jobs
Research Scientist52 jobs
Data Scientist11 jobs
LLM Engineer6 jobs
MLOps Engineer6 jobs
Career Impact
Professionals who combine RAG and scikit-learn earn a median salary ceiling of
$238K, compared to $224K for RAG alone and
$240K for scikit-learn alone. This 2% premium
reflects the salary ceiling is roughly equivalent whether you specialize in one skill or combine both, suggesting the market values either path equally.
Based on our analysis of current AI job postings, 194 positions require both RAG and scikit-learn skills. The most common role for this combination is AI Product Manager.
Jobs requiring both RAG and scikit-learn pay an average of $136K to $238K based on 179 postings with disclosed compensation.
Jobs requiring both RAG and scikit-learn pay $238K on average (max), compared to $224K for RAG alone and $240K for scikit-learn alone. That represents a 2% premium for having both skills.
The most common roles requiring both RAG and scikit-learn are: AI Product Manager, AI/ML Engineer, Research Scientist, Data Scientist. These positions typically involve building production AI systems that leverage both technologies.
Data Source: Analysis based on 1,969 AI job postings collected and verified by AI Market Pulse. Data reflects active job listings as of March 2026. Salary figures represent posted compensation ranges and may not include equity, bonuses, or other benefits.
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Methodology
Skill co-occurrence data is derived from 194 job postings that list both RAG and scikit-learn
as required or preferred skills. Salary data includes only postings with disclosed compensation ranges.
Data is updated weekly from major job boards and company career pages.