MLOps engineers are the platform engineers of the AI org - you build the rails that let dozens of data scientists ship models without each of them re-inventing CI/CD, monitoring, and rollback. Your resume must convince a hiring manager that you've built a system used by other teams, not just deployed your own models.
The strongest MLOps resumes quantify platform leverage: number of models in production, deployment frequency before/after, training cost reductions, mean time to detect drift, and SLOs for inference services. Reference the canonical building blocks - model registry (MLflow, Weights & Biases, SageMaker Model Registry), feature store (Feast, Tecton, Databricks), training orchestration (Kubeflow, Metaflow, Argo Workflows), and inference serving (Triton, KServe, BentoML, Ray Serve). Drift monitoring (Evidently, Arize, WhyLabs) is now table stakes.
ATS keywords: MLOps, Kubernetes, Docker, MLflow, Kubeflow, Airflow, Terraform, Feast, SageMaker, Vertex AI, model serving, model monitoring, CI/CD, GPU, and the cloud you operate in (AWS/GCP/Azure).
This example works for
MLOps Engineer
ML Platform Engineer
ML Infrastructure Engineer
AI Platform Engineer
ML SRE
What you get with every LumiCV resume
- ATS-parseable PDF. Real text, no images - and parse-tested, so every field is recovered in the correct reading order.
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- Match score and keyword gap analysis. See exactly where you match the role and where you're missing before you apply.
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