A bit about me
Writing code is only a small part of the job, and LLMs can generate it in seconds. What matters is systematic problem solving: break the problem into simple pieces, spot where things might break, and decide what makes the most sense to build.
I care less about flashy syntax and more about building things that work in the real world. My focus is on turning ML research into production-grade systems with robust latency, explicit error paths, and clean user interfaces.
Beyond the Screen
Hardware & EmbeddedMy foundation in Computer Engineering isn't confined to serverless containers and cloud clusters. I work with physical compute, embedded electronics, and real-time sensory loops.
Autonomous intelligent lawn mower thesis — Microcontroller integration & motor drivers
Experience
Machine Learning Engineer
Queryfier LLC • Remote
- Architect and deploy production NLP and computer vision models with automated data preparation and model evaluation pipelines.
- Construct low-latency, scalable inference APIs using FastAPI alongside structured Pydantic validation and asynchronous workflows.
ML Engineer & Tutor
Centre for Applied Machine Learning and Data Science • On-site
- Built end-to-end NLP and computer vision pipelines using Scikit-learn and TensorFlow, deploying prototypes via Streamlit and FastAPI.
- Mentored university interns on Python, statistical modeling, and deep learning engineering fundamentals.
Also
Core stack
Interested in discussing an engineering role or technical collaboration?
Get in touch