← All resume profilesMLOps Engineer Resume

MLOps Engineer resume that gets shortlisted

MLOps roles sit between ML and platform engineering. Recruiters scan for ML lifecycle ownership, Kubernetes depth, and production monitoring — not model accuracy alone. We rewrite your resume to prove you ship models, not just train them.

Salary: ₹12–40 LPAGlobal: $120K–$200K16 ATS keywords

What every resume must prove

  • ML pipeline ownership (training → registry → serving → monitoring)
  • Kubernetes + Docker deployment of ML services
  • Experiment tracking with MLflow or Weights & Biases
  • CI/CD for models (GitHub Actions, ArgoCD, Jenkins)
  • Drift detection and model monitoring (Evidently, Prometheus)

What makes you stand out

  • KServe / vLLM serving at scale with autoscaling
  • Feature store experience (Feast, Tecton)
  • GPU scheduling and cost optimization on EKS/GKE
  • RAG + LLM serving in addition to classical ML
  • SLO-driven model rollout and canary deployments

Portfolio projects to put on your resume

End-to-end MLOps pipeline

MLflow · DVC · Kubernetes · GitHub Actions

Cut model deployment time from 2 days to 20 minutes

Drift detection service

Evidently · Prometheus · Grafana

Caught 3 production drift events before user impact

Feature store on Feast

Feast · Redis · Snowflake

Served 200+ features to 5 downstream models with <50ms p99

ATS keywords for this role

MLOpsML pipelineMLflowKubeflowKServeDVCKubernetesDockerCI/CDModel registryDrift detectionFeature storePrometheusGrafanaAWS SageMakervLLM

Include these verbatim where they match your real experience. ATS systems scan for exact terms — but never lie. We help you place them truthfully during the rewrite.

Bullet templates you can copy

1Built MLOps pipeline with MLflow + Kubernetes that reduced model deployment time by {X}%
2Deployed {N} models to production with KServe, achieving {latency} p99 latency
3Implemented drift detection with Evidently, catching {N} issues before user impact
4Owned ML infrastructure on EKS serving {N}M requests/day at {cost} cost

Replace placeholders like {X}, {N}, {latency} with your real numbers.

Common mistakes to avoid

  • Listing model accuracy without deployment context
  • No Kubernetes or containerization evidence
  • Missing monitoring / observability tools
  • Describing notebooks instead of production systems
  • No CI/CD for models — only for app code

Get your MLOps Engineer resume rewritten

Full Resume Rewrite at ₹4,999 (India) or $149 USD (international) — 5 days turnaround, 2 revisions, role-specific ATS optimization. 🎓 Students save 30%.