Applied Machine Learning Engineer
Bridging the gap between experimental research and practical implementation.
My Journey
Specializing in Applied Machine Learning and Research Engineering, I focus on bridging the gap between theoretical research and practical implementation. I leverage my full-stack background primarily to operationalize ML systems, building the necessary tools and interfaces to make models usable and testable.
My expertise lies in developing end-to-end experimental pipelines and internal prototypes. Rather than managing large-scale legacy platforms, I thrive in the iterative process of taking a concept from a research paper and engineering a deployable, reproducible proof-of-concept.
Beyond engineering, I am deeply committed to the open-source community and continuous learning. I actively contribute to research in computer vision and stay engaged with the latest developments in generative AI.
Clean Code
Maintainable, scalable, and self-documenting architectures.
Innovation
Leveraging state-of-the-art AI to solve complex problems.
Collaboration
Effective communication and teamwork in agile environments.
Performance
Optimizing algorithms and pipelines for maximum efficiency.
Technical Expertise
Technologies I've worked with in real-world projects and professional environments
Programming Languages
AI/ML Technologies
Cloud & Databases
Frontend Development
Backend Development
Selected Works
Latent Diffusion Face Anonymization
Experimental pipeline for privacy-preserving anonymization using Stable Diffusion and ControlNet.
PackVote (Optigo)
Prototype AI-powered group travel planner utilizing Ranked-Choice Voting and iterative chunking.
Paligemma Fine-Tuning
Fine-tuning pipeline adapting Google's Vision-Language Model for industrial object detection.
GCP GitOps Pipeline
Reference CI/CD workflow automating deployments from code commit to production on GKE.
Zero-Shot Anomaly Detection
Thesis implementation utilizing VLM (CLIP, PaliGemma) for zero-shot defect detection.
TensorFlow Serving on GKE
Benchmarking scalable ML serving architectures with HPA autoscaling and Locust load testing.
Publications
Research papers and academic contributions.
Federated Learning: A necessity for Agriculture
Exploring the potential of Federated Learning to revolutionize agricultural data privacy and efficiency.
Beyond Syntax: Orchestrating a “Dark Luxury” Portfolio
A deep dive into building a high-end, AI-assisted developer portfolio using Antigravity & Claude.
Professional Journey
Research Assistant
Specialized in privacy-preserving AI for autonomous driving, focusing on Latent Diffusion Models and GDPR compliance.
Research Assistant
Engineered high-precision embedded systems and motion planning algorithms for robotics application.
Endorsements
Feedback from supervisors and research leaders.
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