Las Vegas, NV, USA • Open to remote
Fullstack, Backend, Data Engineering, and ML Systems
I build production-minded backend, data, and ML systems — from reliable pipelines and analytics APIs to deployment-ready inference services. My background spans quantum computing and ML, with a focus on correctness, reproducibility, and measurable impact.
About
Research rigor, engineering outcomes.
A quick overview of how I work and what I optimize for.
I’m a technical professional transitioning from quantum computing and AI research into industry roles in software engineering, ML engineering, and data engineering. My strength is taking research-grade ideas and turning them into production-minded systems: clear interfaces, measurable outcomes, and maintainable code.
I care about correctness and reliability (tests, data contracts, observability), but I’m equally focused on impact — shipping useful features, improving decision quality, and building systems that scale with teams.
I’m most effective when the work benefits from strong systems thinking — reliable data flows, clean service boundaries, and ML workflows that are reproducible and easy to operate.
What I’m optimizing for
- Production mindset — build systems that can be deployed, monitored, and iterated on.
- Clarity — readable code, predictable APIs, and documentation that scales.
- Measurable outcomes — metrics, evaluation, and sane baselines.
Projects
Production-minded builds with clear signals.
A selection of work focused on reliability, clarity, and measurable outcomes. More projects are available on GitHub.
AmCast AI — Amtrak Delay Prediction (In Progress)
End-to-end machine learning system for predicting train delays using historical rail data, combining data pipelines, feature engineering, and API-based inference.
Outcome
Highlights practical ML and data engineering skills by tackling real-world transportation reliability, with a focus on pipeline design, feature construction, and deployable prediction services.
RetentionIQ — Churn Prediction (MLOps-style)
Production-style churn prediction system with drift simulation, automated retraining, FastAPI inference, monitoring, and Dockerized deployment.
Outcome
Showcases end-to-end ML engineering: reproducible runs, operational thinking, and a credible path from training to deployment and monitoring.
Eyeware Funnel Analysis (SQL + Python Case Study)
Case study analyzing user drop-off, A/B test performance, and conversion behavior across a multi-stage onboarding funnel.
Outcome
Surfaces key conversion bottlenecks across the onboarding funnel, quantifies cohort-level behavior, and translates insights into testable, data-driven product decisions.
Language Families — Object-Oriented Modeling in Java
Java-based object-oriented system modeling relationships between language families using inheritance, polymorphism, and linguistic features such as word order.
Outcome
Demonstrates strong understanding of object-oriented design principles and the ability to model real-world hierarchical systems in clean, extensible Java code.
Skills
A toolkit tuned for data + ML systems.
Languages, frameworks, and tools I use to ship and operate systems.
Languages
Frameworks
Data / ML
Cloud / DevOps
Tools
Experience / Education
A timeline built for credibility.
A quick view of the work, study, and focus areas that shaped my engineering practice.
Software & Machine Learning Projects
Independent • Open Source
2024 — Present
- Building production-style systems including data pipelines, ML workflows, and backend APIs.
- Emphasizing scalability, reliability, and clean system design across projects.
- Applying strong analytical and experimental thinking to real-world engineering problems.
Quantum Computing • UC Merced (M.S. in Theoretical Chemistry)
University of California, Merced
2024 — 2026
- Worked on quantum computing, quantum control, and quantum error correction research with strong computational components.
- Built simulation workflows and analysis tooling; emphasized reproducibility and clear reporting.
- Collaborated across disciplines; communicated results to both technical and non-technical audiences during seminars, poster conferenses, and group meetings.
AI • Monash University (MCS in Artificial Intelligence)
Monash University
2026 — 2028
- Incoming MCS student in Artificial Intelligence at Monash University.
- Learning machine learning, deep learning, and NLP to build intelligent systems.
- Developing skills in Python, optimization, and scalable AI system design.
Contact
Let’s talk.
If you’d like to collaborate or talk roles, send a message directly here or reach me by email.