Technical emphasis
- Agent runtimes and multi-step orchestration
- Retrieval workflows and grounded generation
- Evaluation, observability, and reviewer patterns
- Cloud deployment and containerized infrastructure
Resume
Applied AI / ML systems engineer
Work centered on agent runtimes, retrieval systems, infrastructure, deployment, and translating research ideas into reliable systems.
01 / Summary
Focus
Production-minded AI work with emphasis on orchestration, measurement, constraints, and system clarity.
Operating style
- Research translated into measurable engineering systems
- Constraint-aware design instead of prompt-only prototypes
- Implementation depth over vanity metrics
- Calm technical judgment under production requirements
02 / Experience
Roles
Selected roles tied most directly to the current systems profile.
AI Software Engineer
Tabner Inc
Jul 2025 - Present
Designed agentic workflows for internal data systems, improved ETL reliability and runtime, and shipped production container services.
Machine Learning Engineer
Virginia Tech
Aug 2024 - Jul 2025
Built distributed experimentation and genomics model workflows across GPU-backed infrastructure.
Graduate Research Assistant
Virginia Tech
Jun 2023 - May 2024
Worked on efficient model steering, NER improvement, and lower-cost inference paths.
Software Engineer
UJR Technologies
Jul 2021 - Dec 2022
Shipped backend APIs and delivery pipelines with improved integration stability.
03 / Education
Education
Formal training that feeds the current engineering work.
Virginia Tech
Master of Science, Computer Science
Vel Tech University
Bachelor of Technology, Computer Science and Engineering