[NAME] — ML / AI Engineer ([Primary focus])
[City, RO] (optional) · [Email] · [Phone] (optional) · [LinkedIn] · [GitHub] · [Portfolio]

SUMMARY
2–4 lines: what you build (models/pipelines/products), your strongest outcomes, and what you want next.

ML / AI HIGHLIGHTS
- [impact: business metric] using [model/pipeline] in [context]
- [ownership: data quality, evaluation, deployment, monitoring] with a verifiable outcome
- [reliability: drift, latency, cost, privacy] with a verifiable outcome

EXPERIENCE
[Company] — [Role] · [Period] · [City/Remote]
- Impact + context + stack + outcome (metric, latency, cost, quality)
- Ownership (features, training, evaluation, deployment)
- Reliability (monitoring, drift checks, rollback strategy)

[Company] — [Role] · [Period]
- 2–4 bullets

PROJECTS / PUBLIC WORK (optional)
[Project] — [Link]
1–2 lines about what it does and for whom.
- What you built (stack)
- Results / what you learned

SKILLS
ML: …
Data: …
Backend: …
Cloud/MLOps: …
Monitoring: …

EDUCATION & CERTIFICATIONS
[University / Course] — [Period]
[Certification] — [Year]