CV template · MLOps Engineer

MLOps Engineer CV Template

An MLOps engineer takes a model out of the data scientist's notebook and makes it survive in production: stable, cheap, observable, with rollback, with drift monitoring. This template helps you show recruiters the concrete numbers (latency, throughput, infra spend, release cadence) instead of the generic 'worked with ML pipelines' line. Works for entering MLOps from a DevOps or ML background, or for senior engineers targeting a platform role.

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What recruiters look for

Top signals on a MLOps Engineer CV

Numbers in your bullets: inference latency, throughput, GPU utilization, cost per 1000 inferences
Orchestration stack experience: Kubeflow, Airflow, Prefect, Dagster, at least one shipped to prod
Hands-on with GPU infrastructure: A100, H100, autoscaling under traffic
Feature store and model registry you actually deployed (not just "familiar with")
Data and model drift monitoring (Evidently, WhyLabs, Arize)
CI/CD specifically for ML, not just generic DevOps pipelines
At least one cost-optimization story for inference workloads

Key skills

Skills to feature on a MLOps Engineer CV

Hard skills

Kubernetes for ML workloadsDocker, multi-stage GPU image buildsKubeflow or MLflowAirflow / Prefect / DagsterFeature stores (Feast, Tecton)Model serving (Triton, TorchServe, BentoML, vLLM)Terraform for ML infraPython for pipeline codeDrift monitoring (Evidently, WhyLabs, Arize)GPU clusters on AWS / GCP / AzureObservability (Prometheus, Grafana, OpenTelemetry)Vector databases (Pinecone, Weaviate, pgvector)

Soft skills

Comfort at the ML/infrastructure boundaryExplaining infra to data scientists without condescendingCost-thinking by defaultDocumentation treated as part of the jobOn-call ownership of production modelsPatience with GPU OOM at 3 AM

Sample bullets

Ready-to-use lines for your CV

Copy these as starting points and swap in your own numbers.

01Shipped a Kubernetes inference platform for 12 models with autoscaling, holding p99 latency under 80ms at peak 4000 RPS.
02Migrated 4 classification models from GPU to CPU+ONNX, cut $7,400/month in infra spend with no measurable speed loss.
03Built a Feast feature store, dropping idea-to-production time from 6 weeks to 9 days for a team of 5 data scientists.
04Wired Evidently drift monitors with Slack alerts, caught a model degradation 2 days before the business team noticed.
05Migrated MLflow from self-hosted to managed, cutting admin overhead from 6 hours/week to 30 minutes.
06Built a GitHub Actions CI/CD pipeline with automatic shadow tests, dropping failed releases from 3/month to 0.
07Switched autoscaling from fixed replicas to QPS-based, saving $5,200/month on GPU infra.
08Built a self-service portal where data scientists deploy a model in 3 clicks, cutting MLOps tickets by 60%.
09Moved part of the workload from cloud GPUs to on-prem A100s for regulated data, saved $14k/month and passed compliance audit.
10Centralized model storage in MLflow registry, ending the model-scattered-across-S3-buckets era for 14 data scientists.

Salary ranges

What MLOps Engineer earn

2024–2025 estimates. Wide ranges by experience and seniority.

Market

Junior

Mid

Senior

Ukraine

$2,200-3,800 USD/mo

$4,000-6,500 USD/mo

$7,000-10,500 USD/mo

4,000-5,800 EUR/mo

6,200-9,500 EUR/mo

10,000-14,000 EUR/mo

USA

$120,000-160,000 USD/yr

$170,000-230,000 USD/yr

$240,000-340,000 USD/yr

Interview prep

5 questions MLOps Engineer candidates hear

Q1Walk me through how you designed an inference platform from scratch. What tradeoffs did you make on latency vs throughput vs cost?
Q2How do you monitor data and model drift? What threshold for alerts and why that one?
Q3Describe a feature store you deployed. Why Feast / Tecton over rolling your own?
Q4Two models share one endpoint, you need to A/B test in production. How do you build it?
Q5How did you bring inference cost down on a past project? What gave you the biggest single win?

FAQ

Common questions about this CV

How do I move from data scientist into MLOps?

Shortest path: take one of your own models and ship it to production yourself. Docker, Kubernetes deployment, monitoring, the works. Better still as an open-source project you can demo. One full-cycle project tells more than three certifications.

How do I move from DevOps into MLOps?

Learn the model lifecycle: training data → features → training → registry → serving → monitoring. The biggest difference from web DevOps is models degrade on their own even when code does not change. Treat drift monitoring as mandatory, not optional.

Should I target LLMOps over classical MLOps in 2026?

LLMOps pays more right now and the talent pool is thinner. If you have two similar offers, take the LLMOps one. Classical MLOps still has plenty of work but compensation growth is slower.

What do I put in the portfolio if I have no production experience?

Build an end-to-end demo: pick a simple model, deploy it on Kubernetes, add monitoring, ship a shadow-deployed v2. Package the whole thing in one GitHub repo with a strong README. At the interview, open it and walk the architecture.

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