AI/ML Developer Advocate at Google Cloud
Current Role: AI/ML Developer Advocate focusing on AI/ML infrastructure and models.
Working at the intersection of cutting-edge AI technology and developer experience, I help organizations and developers leverage Google Cloud's AI/ML capabilities to build intelligent, scalable systems.
Key Focus Areas:
ā¢ AI/ML Infrastructure & Platform Engineering ā¢ Large Language Models & Generative AI ā¢ Developer Experience & Tooling ā¢ Cloud-Native AI Applications ā¢ MLOps & Model Deployment
Robotics, Mixed Reality, and Software Engineering
Built software systems for robotic platforms, working on perception, navigation, and control systems. Developed solutions that enable robots to interact intelligently with their physical environment.
Created immersive experiences that blend digital and physical worlds. Worked on AR/VR applications that push the boundaries of human-computer interaction and spatial computing.
Core competencies and technologies
ā¢ Deep Learning Frameworks ā¢ Natural Language Processing ā¢ Computer Vision ā¢ Generative AI ā¢ MLOps & Model Serving
ā¢ Google Cloud Platform ā¢ Kubernetes & Containers ā¢ Serverless Architecture ā¢ Infrastructure as Code ā¢ CI/CD Pipelines
ā¢ Python, Go, TypeScript ā¢ API Design & Development ā¢ Distributed Systems ā¢ Microservices ā¢ Test-Driven Development
Teaching, sharing, and community building
Creating technical content that helps developers learn and build with AI/ML technologies.
ā¢ Technical blog posts and tutorials ā¢ Code samples and demos ā¢ Documentation and guides
Sharing knowledge through talks, workshops, and community events.
ā¢ Conference presentations ā¢ Technical workshops ā¢ Meetup talks
Building and supporting developer communities around AI/ML.
ā¢ Open source contributions ā¢ Community support ā¢ Developer feedback loops
Philosophy and principles
"The best technology serves humanity. As a Developer Advocate, my goal is to make powerful AI/ML tools accessible, understandable, and useful for developers building the future."
Every technical decision should consider the developer experience. The best infrastructure is one that developers actually want to use and can be productive with immediately.
AI/ML research moves fast, but production systems have different requirements. Success means translating cutting-edge research into practical, production-ready solutions.
Technology evolves rapidly, especially in AI. Staying current requires constant learning, experimentation, and willingness to challenge existing assumptions.
Building AI responsibly means considering bias, fairness, transparency, and societal impact from the earliest stages of development.