Bernardo Castillo Jr
Senior Staff Software Engineer
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At Kaiser Permanente, I contributed to healthcare technology initiatives focused on improving data accessibility, system integration, and operational efficiency through enterprise software engineering and data analytics.
The work centered on developing scalable solutions for managing complex healthcare information, integrating data across enterprise systems, and supporting more efficient workflows for healthcare professionals and operational teams.
The project showcases how modern software architecture, data processing, and analytics can help healthcare organizations streamline operations, improve information accessibility, and support data-driven decision-making.
Key areas: Healthcare Technology, Software Engineering, Data Integration, Python, SQL, Enterprise Applications, Cloud Computing, and Data Analytics.
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At Cisco, I worked on Threat Detection and Security Analytics, focusing on protecting customers by identifying emerging cybersecurity threats through product telemetry, threat intelligence, and advanced data analytics.
My work involved analyzing large volumes of security data, investigating suspicious activities, and identifying previously unknown threats that could impact customers. I collaborated with security researchers and engineering teams to improve threat detection capabilities, strengthen security monitoring, and turn complex findings into actionable insights.
I also contributed to customer education by developing technical content, including blog posts, videos, and white papers, to raise awareness of emerging threats and security best practices.
Key areas: Cybersecurity, Threat Hunting, Security Analytics, Threat Intelligence, Data Analysis, Security Research, and Customer Protection.
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During my time at Google, I worked as a Tech Lead / Manager on the Ranklab Machine Learning Platform, helping teams across Alphabet solve complex machine learning challenges and build scalable, production-ready ML solutions.
Ranklab evolved from an internal machine learning platform within Google Search into an end-to-end solution supporting data analysis, rapid experimentation, model training, evaluation, and production serving.
My work focused on leading ML engagements, collaborating with engineering and product teams, and developing models that balanced predictive performance with interpretability, maintainability, and long-term reliability.
The platform enabled teams to accelerate model development, streamline experimentation, and integrate machine learning capabilities into their products more efficiently.
Key areas: Machine Learning, Python, Model Training, Feature Engineering, Distributed Systems, ML Infrastructure, Model Serving, and MLOps.
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This project showcases an AI-powered document processing and insights platform designed to streamline how businesses manage, analyze, and extract valuable information from large volumes of documents.
The solution combines Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, and agentic AI workflows to automate document ingestion, intelligent search, summarization, and business processes.
The architecture emphasizes scalability, accuracy, and seamless integration with enterprise systems. It demonstrates my approach to building practical AI solutions that reduce manual effort, improve operational efficiency, and turn unstructured data into actionable insights.
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