About
Building ML-driven AI systems with a focus on architecture and orchestration.
Overview
I'm drawn to problems in ML-driven AI systems from a systems and architecture perspective, particularly where data pipelines, orchestration, and long-running workflows must behave predictably under real-world constraints: systems with predictable failure modes, tractable architectures, and durable operability. I'm less interested in isolated models or surface-level AI features, and more interested in how these components are composed into systems where engineering discipline matters, and where correctness and robustness are as important as raw capability.
I favor explicit contracts over implicit assumptions, and systems designed to be understood, not just built. I'm drawn to novel complexity when it solves real problems, but I'm skeptical of complexity that exists to be clever. When evaluating trade-offs, I lean toward approaches that make failure visible, keep debugging tractable, and leave room for the next engineer to reason about the system without needing its original author.
Outside of formal roles, I actively experiment with ML and AI frameworks, build hands-on projects, and refine my understanding of how AI systems can be structured, evaluated, and maintained in practice. More broadly, I enjoy building systems in general, especially when the work involves clear problem definition, thoughtful design, and turning abstract ideas into working software.
Quick Facts
Technical Focus
Systems & Architecture
- –Service boundaries and APIs
- –Orchestration and control flow
- –Reliability and failure modes
- –System design under constraints
ML & AI Systems
- –ML pipelines and evaluation
- –Model integration and serving
- –LLM-based systems and orchestration
- –Data flow and system observability
Infrastructure & Platforms
- –Cloud-native system design
- –Infrastructure as code
- –Deployment workflows and system observability
Languages & Tools
Python, SQL, Bash, Java, C/C++
Nextflow, SLURM, Docker, Apptainer
PostgreSQL, S3, DynamoDB, Redis, SQLite
AWS (EC2, S3, IAM), Databricks, PySpark
FastAPI, Django, Pandas, PyTorch, Pydantic, pytest, Git, Terraform, GitLab CI
Professional Experience
While my recent roles have been in bioinformatics-heavy environments, my work has consistently focused on production software systems, data pipelines, and infrastructure rather than domain-specific analysis.
Bioinformatician (Software Engineering Focus)
Exact Sciences
Phoenix, AZ
- •Operate and harden high-throughput, distributed AWS and Databricks pipelines processing terabyte-scale workloads, with a focus on reliability, observability, and system-level failure modes.
- •Built validated Python ETL pipelines with 80%+ test coverage to extract KPIs and surface platform bottlenecks.
- •Monitored and optimized distributed pipeline performance using logging, alerting, and iterative tuning.
- •Conducted root-cause analysis for distributed workflow failures, improving system reliability.
- •Designed and deployed Python automation with structured validation and system integration using AI assisted development, cutting manual processing time by 60%.
- •Partnered with cross-functional stakeholders to translate business requirements into technical specifications.
Bioinformatics Analyst, Intern
University of Arizona COM Phx Child Health
Phoenix, AZ
- •Built reproducible, containerized data workflows for multi-terabyte datasets using Docker, Apptainer, and Nextflow.
- •Developed modular, testable components to support long-running SLURM jobs on shared HPC infrastructure.
Research Technician
Biodesign Institute at Arizona State University
Phoenix, AZ
- •Migrated large CSV-based datasets into PostgreSQL, replacing Excel-based workflows and significantly improving query performance and usability for datasets exceeding 70k records.
- •Automated data ingestion and processing pipelines using Python, reducing manual processing time from hours to minutes per batch.
- •Built a robust ETL system to extract, validate, and normalize sequencing metadata from public sources with 97.4% reliability.
- •Authored technical documentation and onboarding materials to support maintainability and knowledge transfer.
Background & Foundations
Computer Science (B.S., in progress)
Arizona State University
Tempe, AZ
Focused on software systems, algorithms, and machine learning, with increasing emphasis on ML systems, orchestration, and large-scale software architecture.
Biochemistry & Psychology (B.S.)
Arizona State University
Tempe, AZ
Formal training in biological systems, experimental thinking, and human cognition. This background shapes how I reason about complex systems, failure modes, and behavior at scale.