Jordan Harris
Machine Learning Engineer @Lifebonder
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WORK HISTORY
Machine Learning Engineer @Lifebonder
Berlin, DE
Established a core ML and Data Science hub using Microsoft Azure tools (AI Foundry, Synapse Spark, Azure Functions) for data governance and orchestration of our ML & Analytics pipelines. This provides our ML & Data Science team w/ a self-service data and feature platform which funnels all data through a medallion architecture that is enforced using DBT transformations & a Data Tagging Plan for quality- Architected 3 core data pipelines; A real-time Analytics pipeline ingesting raw (Bronze) unstructured app data (user activities, geolocation, messages, ect) which feeds daily preprocessing and feature engineering jobs (silver: OpenAI-powered feature extraction). Enabling two downstream pipelines: online model training and inference for risk-aware recommendation (TorchRec) and content moderation. These models are then evaluated, stored & versioned using the MLFlow + AI Foundry integration to spin up compute resources, environments & endpoints to then generate recommendable content and user affinity buckets for the marketing team, as well as flag content for community managers (Gold). This was all achieved under tight resource constraintsvia Microsoft’s Founders Hub grant- Ensured user safety by anonymizing user data, activities, locations, and messages, upholding privacy as a core Lifebonder value while maintaining GDPR compliance.
EDUCATION
St. John's University
Bachelor of Arts (BA), Biology, General
Universitat Pompeu Fabra
Master of Science - MS, Intelligent Interactive Systems
New York University
Master's degree, Microbiology and Immunology
SKILLS
ABOUT JORDAN HARRIS
ML/MLOps/Data Engineer with a documented background in Data Governance (QA, Data Analytics & Data Engineering). My master\'s thesis in my Intelligent Interactive Systems degree was on NLP/Implicit Sentiment Analysis/CoT Reasoning and its application within social networks through Web Intelligence techniques. In parallel, I also focused my courses on Ethics, Psychology, and Sociology. In the future, I hope to combine and leverage my expertise in data governance, sociology, and NLP/NLI for core AI/ML teams, or within data teams for commercial, medical, and content marketing fields.Alongside my professional work, I pursue projects in agentic AI, data equity, and context-aware systems — connecting governance and reliability with modern approaches like orchestration, knowledge graphs, and balanced datasets. I aim to build AI systems that are not only scalable and reliable, but also equitable and socially aware.
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