Responsibilities:

📍Build automated SEO monitoring and diagnostic systems using Python, BigQuery, and ML frameworks to analyze 500K+ pages at scale 📍Develop LLM-optimized content strategies targeting AI Overviews, ChatGPT, Perplexity, and other generative search platforms 📍Design and implement ML-based internal linking recommendation engines (graph databases, semantic similarity models) to replace manual link building 📍Create automated content quality assessment systems using NLP/LLM APIs to identify thin content, duplicate issues, and optimization opportunities 📍Build custom SEO APIs and MCP servers integrating Google Search Console, Ahrefs, DataForSEO, and internal analytics for Claude/AI agent workflows 📍Develop Python scripts for log file analysis, crawl pattern optimization, and automated technical SEO auditsEngineer real-time performance dashboards combining organic traffic, conversion attribution, and competitive positioning metrics 📍Implement E-E-A-T signal optimization through automated authorship systems, schema markup generation, and structured data validation 📍Train and deploy ML models for keyword clustering, search intent classification, and cannibalization detection across multi-language content 📍Build automated QA frameworks for SEO validation in CI/CD pipelines (schema testing, hreflang validation, indexation checks)

Requirements:

📍5+ years combining software engineering/ML with hands-on SEO execution (not just theory) 📍Strong experience with BigQuery, SQL, and data warehouse architectures for SEO analytics 📍Strong Python programming experience: pandas, scikit-learn, API integrations, web scraping, data pipeline orchestration 📍Understanding of NLP, semantic search, entity recognition, and knowledge graph concepts 📍Deep understanding of LLM architectures, prompt engineering, and RAG systems for search optimization 📍Proven experience building SEO automation tools that drive measurable organic traffic growth

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