I’m an AI Engineer at Neurons Inc., where I get to blend neuroscience, machine learning, and a lot of creative problem-solving. I build and experiment with all kinds of AI systems — from backend services and APIs to mini apps that do fun things like find similar images using embeddings, train models to predict ad memorability with EfficientNet and similar, or detect areas of attention with YOLO.

 

On the backend side, I design and develop scalable API endpoints and services that connect models to production — turning experiments into usable tools. I also work on prompt engineering for large and multimodal models, creating pipelines that generate visual insights, image transformations, and creative outputs from structured prompts. My process always includes performance analysis and modeling — combining classic statistics with LLM-based reasoning and summaries to uncover deeper patterns in results. It’s a mix of data, intuition, and engineering — where math meets imagination.

 

When it comes to AI, I believe in building human-centered intelligence. I try to think simply (focus on outcomes), think contextually (embed AI in natural workflows), and think big (aim for complex reasoning, not just automation). I like systems that infer, reason, and guide — proactive, responsible, and purposeful. For me, great AI feels almost invisible — quietly powerful, deeply contextual, and always designed with humans in mind.

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