Hi, I'm Joe Doe

Welcome to my world of innovation and purpose, where software, medicine, and AI converge to shape the future of human health.

CONTACT INFORMATION

Name: Joe Doe | Location: Austin, TX, USA | Phone: +1 (555) 123-4567

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PROFESSIONAL SUMMARY

Software Engineer with strong experience in full-stack development, cloud architecture, and machine learning. Currently pursuing a Doctor of Medicine (MD), integrating medical knowledge with advanced software solutions to develop innovative healthcare technologies. Passionate about AI-driven diagnostics, medical data engineering, and clinical software tools that improve patient outcomes.

• Programming: Python, JavaScript, TypeScript, Java, C#
• Frameworks: React, Node.js, Django, Flask, .NET Core
• Cloud: AWS, Azure, Google Cloud
• Databases: PostgreSQL, MongoDB, MySQL
• Tools: Docker, Kubernetes, Git, CI/CD pipelines
• Special Interests: AI in healthcare, medical data processing, digital health platforms

• AWS Certified Solutions Architect
• Azure AI Fundamentals
• Google Professional Machine Learning Engineer

• English (Native)
• Spanish (Professional Proficiency)

Available upon request.


EDUCATION
  1. Expected Graduation: 2029 Doctor of Medicine (In Progress)

    Austin School of Medicine — Austin, TX. Focus: Internal Medicine, Medical Technology, Clinical Informatics

  2. Graduated: 2023 Bachelor of Science in Software Engineering

    University of California — Berkeley, CA. Honors: Cum Laude, Dean’s List (4 semesters)



PROFESSIONAL EXPERIENCE
  1. 2023–2025 | Austin, TX Software Engineer — TechNova Systems

    Developed microservices in Python and Node.js supporting healthcare data pipelines. Implemented AI-driven tools for clinical record analysis, improving processing speed by 37%. Designed and deployed cloud infrastructures across AWS and Azure. Applied compliance standards (HIPAA, HL7) during development.

  2. Summer 2022 | San Francisco, CA AI Engineering Intern — MediCore Analytics

    Built machine learning models for cardiovascular anomaly detection. Increased model accuracy by 12% using optimized features. Supported clinical dashboards built with React and D3.js.



PROJECTS

• ML-based tool that predicts triage levels from symptoms.
• Built with Python, TensorFlow, and FastAPI.

• Encrypted platform for managing patient records.
• Uses Kubernetes, Docker, and AWS Lambda.


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