An ML/AI CV should show what you built, how you measured it, and how you shipped it safely (data quality, monitoring, drift, latency).
See the general guide: IT CV template (Romania).
TL;DR
- Recruiters scan for impact + evaluation + production readiness.
- “Trained a model” is not enough; include data, metrics, baseline, and deployment story.
- If you don’t have metrics, use verifiable signals: better reliability, fewer incidents, faster iteration.
Quick checklist (before you send)
- Title: “ML Engineer” / “Data Scientist” / “Applied Scientist”.
- Mention your strongest focus: NLP, CV, recommender systems, forecasting, MLOps.
- If you have public work: keep it clean (README, reproducible steps, results).
- For production work: call out data quality, evaluation, deployment type (batch/real‑time), and monitoring.
Recommended structure (ML / AI)
- Header (clean links)
- Summary (2–4 lines: domain + model types + what you’re targeting)
- Experience (impact + evaluation + production story)
- Selected projects (recommended for juniors/switchers; include real results and limitations)
- Skills (ML, data, MLOps, cloud)
- Education/Certs (short)
What a strong bullet looks like (ML / AI)
Useful formula: Outcome + context (data/volume) + approach + evaluation (baseline/metric) + production (if applicable).
Examples:
- “Reduced false positives by ~18% with an XGBoost model, validated via PR‑AUC and tested online with a rollback plan.”
- “Cut inference cost by ~25% using batching + quantization while keeping quality within agreed bounds.”
- “Added data quality checks for critical inputs, reducing data‑driven incidents after release.”
No numbers? Use signals:
- reproducible experiments, faster iteration, drift alerts, shorter time‑to‑detect, standardized pipelines, fewer production regressions.
Bullet library (ML / AI)
Pick 6–10 that are actually true for you, then tailor them to the role.
Outcomes (business and product)
- “Improved [metric] by [X%] by redesigning [feature/pipeline] (baseline → new approach).”
- “Reduced model inference cost by [X%] with batching/quantization/pruning.”
- “Cut time-to-ship experiments by [X%] with a standardized training/eval pipeline.”
- “Increased adoption of [feature] by improving ranking/recommendations and measuring impact online.”
- “Brought latency under [SLA] by optimizing inference and using caching where appropriate.”
Data & evaluation
- “Introduced data quality checks, reducing silent failures and bad training data.”
- “Built evaluation suite (offline + online), improving confidence before release.”
- “Added dataset versioning and reproducibility, reducing ‘it changed’ debugging.”
- “Designed a split strategy (time‑based / user‑based) to avoid leakage and get realistic evaluation.”
- “Established simple baselines first, then iterated on features/model selection with controlled experiments.”
- “Documented model limitations and edge cases where performance degrades.”
Deployment & monitoring
- “Shipped model as an API/service with monitoring and rollback strategy.”
- “Added drift monitoring + alerts, reducing time-to-detect quality degradation.”
- “Optimized latency to hit [SLA], enabling real-time usage in production.”
- “Used shadow deployments to compare new vs old outputs before rollout.”
- “Defined alert thresholds for input/output quality and set up dashboards for the team.”
MLOps & reproducibility
- “Standardized training with configs + experiment tracking (artifacts + metrics), improving reproducibility.”
- “Versioned datasets/models and automated retraining when it made sense.”
- “Reduced model‑to‑production time with CI checks for ML (tests + validation gates).”
LLM / prompting (only if relevant)
- “Built a prompt evaluation set (questions + rubric) to reduce regressions between iterations.”
- “Added guardrails (PII, policy, safety) and fallbacks for low‑confidence cases.”
- “Optimized cost by caching, batching, and using the right model for each task.”
Common mistakes
- Listing models/algorithms without saying what problem they solved.
- No mention of evaluation, baselines, or how you prevented regressions.
- Public repos without results, datasets, or clear setup instructions.
Useful keywords (use only what you actually did)
- feature engineering, baselines, cross‑validation (when appropriate)
- metrics: precision/recall/F1, PR‑AUC, RMSE/MAE, NDCG (depends on the problem)
- data quality, leakage, drift monitoring
- batch vs real‑time inference, latency, SLA
- model versioning, experiment tracking, reproducibility
- CI for ML, rollback, shadow deploy
- LLM: prompt evaluation, guardrails, retrieval (RAG) (only if real)
ML / AI CV template (copy/paste)
FAQ
Should I call myself a Data Scientist or an ML Engineer?
If the job is about production, integration, monitoring, and reliability, “ML Engineer / Applied ML” usually fits. If it’s more about analysis, modeling, and experimentation, “Data Scientist” often fits better. Use the title that matches what you’ve actually done.
I only have projects (no commercial experience). Is that okay?
Yes, if you treat them like deliveries: data, evaluation, results, limitations, and how you’d ship/monitor in production.
