Hansie Rivera

Technical founder and engineering leader with 15+ years of experience architecting consumer-facing AI products, intelligent recommendation engines, and full-stack MLOps infrastructure. Creator of Savannah, a generative AI-driven real estate platform that reimagines property discovery through conversational intent mapping, personalized trade-off logic, and persistent user state-tracking. Expert at bridging deep machine learning pipelines (NLP, LLM fine-tuning, RAG) with highly responsive, consumer-grade cloud architectures.

Founder & Principal AI Architect (Savannah - A More Human Home Journey) at TELLA SAVANNAH (2024 – Present)

Conceived, built, and launched an end-to-end conversational AI web application designed to replace traditional real estate parametric search filters with natural language semantic discovery.

• Conversational Intent Engine: Architected a stateful NLP orchestration pipeline that parses natural language inputs (e.g., location, budget, lifestyle trade-offs) to extract structured user preferences and intent parameters.
• Contextual Recommendation Engine: Built a proprietary multi-attribute optimization system that balances hard budget boundaries against quality-of-life preferences (e.g., yard space vs. financial headroom), generating dynamic text explanations for why properties fit.
• Full-Stack Product Delivery: Engineered the entire system stack, integrating a responsive, stateful web front-end with real-time streaming backend inference APIs via persistent user session databases.
• B2B Marketplace Integration: Designed and built a secondary portal (agents.tellsavannah.com) that distills consumer conversational intent history into structured, lead-authorized context summaries for real-world transaction enablement.

Aerospace Digital Solutions Senior Manager (AI/ML & Data Engineering) at EATON (2023 – Present)

Direct technical architecture and execution teams building enterprise-grade MLOps pipelines and distributed predictive modeling architectures across global business domains.

• Unified MLOps Platform: Designed a production-ready model deployment infrastructure leveraging Databricks, automated CI/CD pipelines, and MLflow, shrinking model deployment lifecycle velocity by 80%.
• Distributed Analytics: Deployed distributed computing pipelines using PySpark to process multi-terabyte internal datasets, transforming cold telemetry metadata into production inference systems.

Legal Data Science Manager (Applied AI & Analytics) at DELL TECHNOLOGIES (2021 – 2023)

Led the algorithmic transformation and data strategy for a global 400-person organization, standardizing codebases and technical roadmaps.

• NLP Automation Pipelines: Deployed production NLP pipelines optimizing unstructured corporate document parsing, directly eliminating manual tracking backlogs.
• Data Foundation Scale: Consolidated multi-platform data silos into high-performance cloud databases, establishing foundational feature sets for operational predictive systems.

Lead Data Scientist (Advanced Analytics & Operations) at BRIDGESTONE (2018 – 2021)

Headed a machine learning engineering unit developing real-time anomaly detection and predictive supply metrics.

• Real-Time Predictive Modeling: Deployed time-series forecasting frameworks and streaming telemetry pipelines processing millions of daily data rows, cutting product variance and downtime.
• Bridgestone President Award (2021) awarded for engineered operational cost reductions.

Master in Data Science – Lipscomb University (2019)

Bachelor of Science in Business Administration – Lipscomb University (2017)