Software Engineer with a Master of Science in Computer Science from Stevens Institute of Technology (December 2025), focused on building reliable, production-ready systems at scale.
I specialize in distributed systems, cloud infrastructure, and applied machine learning - designing systems that operate under real-world constraints where performance, observability, and fault tolerance matter as much as functionality.
I work at the intersection of backend systems, Machine Learning, Computer Vision, and MLOps - designing infrastructure to train, deploy, and operate models in production. Beyond model development, I care deeply about latency, availability, versioning, monitoring, and deployment safety, ensuring ML systems remain dependable at scale.
I approach engineering with discipline, structure, and first-principles thinking. I believe strong intuition is built through strong applied knowledge - earned by understanding how systems behave under load, how they fail, and how they can be improved.
- 🔭 Working on: Building scalable ML inference pipelines and distributed backend services
- 🌱 Learning: Advanced system design patterns, Rust for systems programming, and large-scale data processing
- 💬 Ask me about: Distributed systems, ML infrastructure, cloud architecture, and backend engineering
- 🎯 Goal: Contributing to high-impact engineering teams at top tech companies
| Languages |
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| ML / AI |
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| Cloud & Infra |
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| Backend & Data |
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| DevOps & Observability |
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I'm actively seeking Software Engineer, Backend Engineer, ML Infrastructure, and Platform Engineer roles where I can contribute to building systems that operate reliably at scale. I'm particularly interested in teams working on distributed systems, ML infrastructure, cloud-native platforms, and high-throughput data pipelines.
Open to: Full-time roles · U.S.-based · Remote or On-site
"First, solve the problem. Then, write the code." — John Johnson