Julian Ortega

Staff Engineer

• Designed Kubernetes migration architecture for a core search service of 500+ servers, building a working PoC that required zero downtime or changes to upstream systems
• Continuing development of agentic AI products and cross-team technical leadership on recommendation and discovery systems

Staff Engineer

• Evolved conversational commerce from research to production, adopting a single-agent-with-tools architecture at 20x lower LLM cost than the initial vendor, validated as the pattern later adopted by leading AI providers
• Built AI knowledge assistants targeting ~18% of all support ticket volume, with internal trial validating ~80% documentation coverage and a 30% ticket deflection target
• Led architectural redesign of the suggestion ranking system, reducing index processing times by 64-93% and improving suggestion relevance by 15-18% in customer quality testing
• Contributed to AI-powered synonym generation system achieving 93% human-level accuracy in early customer testing
• Owned service lifecycle end-to-end: model deployment, real-time inference monitoring, CI/CD, evaluation metrics, and cross-functional delivery with product, sales, and customer success

Software Architect

• Architected AI-powered semantic search adopted by enterprise retailers across fashion, sports, grocery, and DIY verticals, supporting 40+ languages
• Led vector search scaling investigation across 4 backends, identifying the cost-viable architecture for enterprise-scale catalogues
• Built serving infrastructure handling 100x traffic spikes through autoscaling architecture validated during production incidents
• Drove CI/CD optimization delivering 3x build speedup at lower infrastructure cost across the engineering organization
• Founded the Software Architecture Group, establishing engineering standards, code quality analysis across 5 product lines and 2M+ lines of code, and a 7-level dual-track career ladder

Team Leader / Line Manager

• Designed and led implementation of the Adaptive Personalization Engine (APE) from PoC to production, building a microservices architecture with 6 core components processing real-time user activity streams at 200+ events/second across 4 major releases
• Delivered personalized product recommendations and search re-ranking while maintaining search response impact under 50ms average
• Initiated and led the search modernization project, reducing reindex times from 2+ hours to 26 minutes and improving search response from 192ms to 130ms; achieved 99.8% of queries under 200ms
• Coordinated cross-functional delivery across 8+ stakeholder teams including operations, infrastructure, QA, sales, and professional services
• Mentored junior developers and facilitated career growth, with 70% of team members receiving promotions during tenure

Senior Software Engineer

• Created real-time recommendations engine proof of concept, integrating it into the core e-commerce product and establishing the foundation for production recommendation systems
• Optimized product catalog ETL processes for 32M+ product catalogues, reducing reindexing time from 40 hours to 4 hours through data partitioning and performance improvements
• Led major upgrade of internal ETL framework spanning 4 major platform versions, enabling the Data team with modern tooling
• Developed ETL processes for user profile indices used in personalized merchandising

Software Engineer

• Developed and maintained search and recommendation algorithms processing terabytes of e-commerce data
• Implemented collaborative filtering techniques that improved recommendation accuracy across multiple client implementations
• Worked on large-scale data processing systems, optimizing performance for real-time recommendation delivery

R&D Intern

• Automated and industrialized evaluation process of recommendations, creating frameworks still used in production systems
• Generated new recommendation approaches based on collaborative filtering, contributing to patent applications
• Researched machine learning techniques for recommendation systems, bridging academic research with commercial applications

Adaptive Personalization Engine (PoC to Production)

• Created the original proof of concept and evolved it to production across 4 major releases over 2.5 years
• Built microservices architecture with 6 core components processing real-time user activity streams at 200+ events/second
• Maintained search response time impact under 50ms average while delivering personalized recommendations
• Coordinated cross-functional delivery across 8+ stakeholder teams

AI-Powered Semantic Search Platform

• Adopted by enterprise retailers across fashion, sports, grocery, and DIY verticals, supporting 40+ languages
• Led vector search scaling investigation across 4 backends, identifying the cost-viable architecture for enterprise-scale catalogues
• Architected dual delivery model: standalone hybrid search engine and AI Search plugin using shared embedding technology
• Handled 100x traffic spikes through autoscaling architecture validated during production incidents

Conversational Commerce Platform (Agentic ML)

• Self-initiated from research through PoC to production deployment, providing a narrative and live demo that supported new prospect engagement
• Evolved architecture from modular intent routing to a single-agent-with-tools pattern with templated prompts and emotional tone adaptation, later validated as the approach adopted by leading AI providers
• Evaluated LLM providers and reached a 20x lower cost than the initial vendor, with gains in intelligence, tool use, and speed
• Built tools and infrastructure to allow agents to reason using customer search and merchandising engines

AI-Powered Knowledge Assistants (RAG + Agentic)

• Targeted the largest addressable support category (~18% of all ticket volume), reducing resolution friction for common how-to questions
• Internal trial validated ~80% documentation coverage for real customer questions
• Designed for measurable ticket deflection with a 30% reduction target, directly reducing average resolution times of 20+ days
• Processed and embedded ~900 knowledge base pages and anonymized customer tickets into the retrieval pipeline

Search Modernization Project

• Reduced search reindex times from 2+ hours to 26 minutes across three release iterations
• Achieved 99.8% of queries under 200ms for mid-size catalogues
• Improved average search response from 192ms to 130ms on the platform's most demanding deployment
• Delivered 84% test coverage across unit and integration tests
• Architected hot-swap reindex strategy enabling zero-downtime index updates

Master's in Computer Science (AI Specialization)

B.S. in Systems Engineering