If I can't add value in the first week, don't pay me.

About Me

I'm an AI Engineer focused on building intelligent systems that go beyond prototypes — production-grade pipelines that fuse machine learning, automation, and full-stack engineering into real, deployable products. My work sits at the intersection of applied AI research and scalable backend architecture, with a growing focus on systems-level thinking as I move deeper into performance-critical and low-level programming.

My core belief is that AI is only as valuable as the system it's embedded in. A model is a component, not a product — the real engineering challenge is turning research-grade intelligence into something reliable, explainable, and usable at scale. That philosophy has shaped everything from a multimodal interview assessment engine achieving 0.92 correlation with human judgement, to explainable job-matching systems, to automation pipelines processing tens of thousands of assets autonomously.

What I Build

• Multimodal AI Systems — fusing computer vision, audio, and NLP signals (facial emotion recognition, gaze estimation, acoustic analysis, transformer-based language understanding) into unified, explainable decision pipelines
• Explainable & Trustworthy AI — models that don't just predict, but justify their outputs, using techniques like SHAP and semantic embeddings to make AI decisions auditable
• Intelligent Automation — end-to-end systems that remove humans from repetitive loops entirely, from data ingestion to structured delivery, using Python, LLM integrations, and workflow orchestration
• Production Backend Systems — scalable APIs and real-time services built to hold up under real users, not just demos

Current Direction

I'm currently deepening my systems-level foundations in C, C++, and Assembly — not as a departure from AI work, but as preparation for building faster, leaner, and more efficient ML infrastructure. In parallel, I continue developing research-driven AI products, including a multimodal candidate assessment platform (published research, IEEE Access track) and an open-source resume automation system now adopted by a startup for internal use.

I care less about chasing every new framework and more about depth: understanding why a system works, so I can build ones that are explainable, efficient, and genuinely useful — not just impressive in a demo.