Professional Overview
I'm an AI engineer, passionate about the subject so if the role fits my experience, i have something to contribute, if it does not, i have something to learn. My background is in computer vision but I would like to expand my knowledge based on how useful i could be from the market's perspective.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Architected AI pipelines for realistic flooring and wall material visualization
- Deployed TensorRT inference pipelines on GPU infrastructure for low-latency apps
- Built FastAPI microservices for model serving and inference orchestration
- Developed GenAI and RAG systems for content generation
- Designed image inpainting and computer vision workflows
Key Achievements / KPIs
- Reduced inference latency by 20% through TensorRT optimization
- Delivered real-time AI visualization on production GPU systems
- Built scalable RAG pipelines with PostgreSQL and Qdrant
- Automated content generation from natural language queries
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Architected AI pipelines for realistic flooring and wall material visualization
- Deployed TensorRT inference pipelines on GPU infrastructure for low-latency apps
- Built FastAPI microservices for model serving and inference orchestration
- Developed GenAI and RAG systems for content generation
- Designed image inpainting and computer vision workflows
Key Achievements / KPIs
- Reduced inference latency by 20% through TensorRT optimization
- Delivered real-time AI visualization on production GPU systems
- Built scalable RAG pipelines with PostgreSQL and Qdrant
- Automated content generation from natural language queries
Education
Field of Study: Machine Learning
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
