I am an M.S. Information Technology student in Virginia, graduating in April 2027. I build cloud-ready backend, data, and applied AI systems where the engineering evidence is visible: typed contracts, failure handling, tests, observability, evaluation, and documented tradeoffs.
My 2027 focus is software engineering, backend/platform, cloud, data engineering, and applied AI/ML. Internship work authorization is coordinated through my university's CPT process and confirmed for each role before applying.
FlightOps AI is an airline-operations intelligence API for retry-safe flight-event ingestion, explainable operational risk, and evaluated T-24h arrival-delay prediction.
official BTS data -> chronological evaluation -> portable model -> versioned API
| Proof | What is implemented |
|---|---|
| Live API | Interactive OpenAPI documentation on Vercel |
| Reliability | Idempotent ingestion, validation, health checks, and metrics |
| Engineering | Eight automated tests, Docker, CI, architecture decisions, and scoped production deployment |
| Evaluated ML | 90,000 official BTS records; future-period test; ROC-AUC 0.640; PR-AUC 0.335; recall 0.748; Brier 0.176 |
| Model integrity | Leakage-resistant T-24h contract, calibration, carrier/airport/time error slices, model card, and inspectable JSON inference |
Stack: Python 路 FastAPI 路 Pydantic 路 SQL/SQLite 路 Docker 路 GitHub Actions 路 REST/OpenAPI 路 Vercel
Airline Data Reliability Lab is an observable, quality-gated airline batch pipeline that records what loaded, what failed, and why.
CSV -> schema + business checks -> quality gate -> facts or quarantine -> API + metrics + report
| Proof | What is implemented |
|---|---|
| Data quality | Required-schema checks, reason-coded row quarantine, and fail-closed batch thresholds |
| Reliability | Deterministic fact keys make source retries idempotent; every run records its source SHA-256 |
| PostgreSQL path | SQLAlchemy schema, PostgreSQL 16 Compose environment, run/check/fact/quarantine tables |
| Verification | Seven tests, passing CI, FastAPI evidence endpoints, Prometheus metrics, and a generated report |
Stack: Python 路 FastAPI 路 PostgreSQL 路 SQLAlchemy 路 Docker Compose 路 GitHub Actions 路 Prometheus
- Turn the two shipped systems into tailored resume bullets and role-specific application evidence.
- Confirm CPT dates and limits with the university DSO before accepting or starting any internship.
- Add AWS/Terraform depth only after the application pipeline is active and tracked.
| Systems | Data + AI | Delivery |
|---|---|---|
| Python, FastAPI, REST, SQL | Data contracts, baselines, evaluation | Docker, CI/CD, cloud deployment |
| TypeScript, React | Retrieval, embeddings, model cards | Observability, failure handling, documentation |
- Ship evidence, not keyword lists. Every featured skill should point to code, tests, a live system, or measured results.
- Design for failure. Retries, duplicate events, invalid inputs, degraded dependencies, and rollback paths are part of the product.
- Measure AI honestly. Start with a baseline; report calibration, latency, errors, and limitations before claiming improvement.
- Keep provenance clear. External projects are learning references or attributed forks鈥攏ot cosmetically modified work presented as original.
status: internship_application_ready
shipped_projects: 2
flagship: flightops-ai-v0.2
current_priority: tailored applications + targeted outreach
target_roles:
- software engineering
- backend / platform engineering
- cloud / data engineering
- applied AI / ML engineeringOpen to 2026-2027 U.S. internship conversations in cloud, backend, data, and applied AI.