Las Vegas, NV, USA • Open to remote
Full-Stack SWE, Data Engineering, and ML Systems
Entry-level backend and MLOps/AI engineer and data scientist with experience shipping distributed systems, decision engines, and data pipelines in production-adjacent environments. Focuses on clear service, measurable performance, and writing tested, extensible and reproducible code comfortable navigating large codebases and contributing across teams.
About
Research rigor, engineering outcomes.
A quick overview of how I work and what I optimize for.
Working at the intersection of software engineering, machine learning, AI, and data engineering and my main focus is turning complex ideas into practical, production-ready solutions. My strength is taking research-grade ideas and turning them into production-minded systems: clear interfaces, measurable outcomes, and reliable code.
I care about correctness and reliability but I’m equally focused on impact — shipping useful features, improving decision quality, and building systems that scale.
I’m most effective when the work benefits from strong systems thinking; reliable data flows, clean service boundaries, and ML workflows that bank on reproducibility.
What I’m going 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.
RailCast AI: BART Transit Delay Prediction System
Provides real-time transit delay predictions by combining live GTFS data directly from BART with schedule context, ensuring accurate arrival estimates even when live signals are sparse.
Impact
Engineered a layered prediction stack (live feed → model → schedule), measurable model quality, sub-13ms warm inference after optimization, and disciplined release automation across Python, Java, and frontend builds.
JPMorgan Chase Advanced Software Engineering Program
Real-time transaction processing system (Midas Core) which processes and validates financial transactions in real time using an event-driven architecture, maintaining account balances, applying incentives, and exposing up-to-date account state via APIs.
Impact
Engineered an event-driven banking-style flows: safe consumption of async messages, transactional balance updates, and coordinated HTTP side effects with testable Spring components.
RetentionIQ: Subscriber Churn Prediction System
Production-style MLOps churn prediction system with drift simulation, automated retraining, FastAPI inference, monitoring, and Dockerized deployment.
Impact
Engineered an end-to-end ML engineering: reproducible runs, operational thinking, and a credible path from training to deployment and monitoring.
A/B Testing & Conversion Analysis (SQL + Python Case Study)
Case study analyzing user drop-off, A/B test performance, and conversion behavior across a multi-stage onboarding funnel.
Impact
Engineered funnel analytics to surface key conversion bottlenecks across the onboarding flow, quantify cohort-level behavior, and translate insights into testable, data-driven product decisions.
Skills
A rapidly growing and expanding toolkit.
Languages, frameworks, and tools I use to ship and operate systems.
Languages
Frameworks
Data / ML
Cloud / DevOps
Tools
Experience / Education
Quick snapshot of my experience.
A quick view of the work, study, and focus areas that shaped my engineering practice.
AI/ML Engineering Extern
Pfizer (via Extern HQ)
Mar 2026 – Present
- Developing a multi-engine OCR document processing pipeline in Python (OpenCV, PIL, Tesseract, PaddleOCR, EasyOCR): preprocessing scanned documents and emitting structured JSON with field-level coordinates across multiple document types.
- Benchmarked three OCR engines against real pharmaceutical scans; identified accuracy divergence by document type and delivered a data-backed engine recommendation.
- Designing document classification and routing logic in Python to categorize incoming files and dispatch them to the correct extraction pipeline: reducing processing ambiguity and enabling modular extension.
- Building a RAG retrieval system using LlamaIndex, FAISS/Chroma, and metadata filtering over Gemini and open-source LLMs, surfaced through a Gradio/Streamlit interface.
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.
AI • Monash University (MCS in Artificial Intelligence)
Monash University
2026 — Present
- Incoming MCS student in Artificial Intelligence at Monash University (Remote, Offshore).
- Learning machine learning, deep learning, computer vision, and NLP to build intelligent systems.
- Developing skills in Python, optimization, and scalable AI system design.
Quantum Computing • UC Merced (MS in Computational Chemistry)
University of California, Merced
2024 — 2026
- Worked on quantum computing, quantum control, quantum molecular simulations, 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.
Contact
Let’s talk.
If you’d like to collaborate or talk roles, send a message directly here or reach me by email.