MLOps Engineer Resume Template

A template built for MLOps and ML platform roles — designed to surface model deployment work, training pipeline ownership, monitoring discipline, and the production ML metrics hiring managers want to see.

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Priya Venkatesh

priya.venkatesh@email.com|(415) 555-0193|linkedin.com/in/priyavenkatesh-mlops

Summary

MLOps engineer with 5 years building production ML platforms. Currently at Stripe, where I own the training-to-serving pipeline for 28 production models, cut median deployment time from 6 days to 4 hours, and run weekly drift monitoring across 11 high-traffic endpoints.

Experience

MLOps Engineer Mar 2023 – Present

Stripe San Francisco, CA

Owned the training-to-serving pipeline for 28 production models across fraud, risk, and personalization, supporting 4 ML teams with a shared platform built on Kubeflow + Vertex AI
Cut median model deployment time from 6 days to 4 hours by replacing the manual handoff process with a GitHub Actions-based CI/CD pipeline that runs validation, shadow eval, and canary rollout automatically
Built drift monitoring (data drift + prediction drift + label drift) on 11 high-traffic model endpoints, surfacing 4 model degradations in 2025 before they hit a customer-facing SLO
Migrated the feature store from a custom Redis-backed system to Feast on BigQuery, eliminating training-serving skew across 6 models and cutting feature retrieval p99 from 280ms to 42ms

Machine Learning Engineer (Platform) Jun 2021 – Feb 2023

Datadog New York, NY

Built the experiment tracking infrastructure (MLflow + custom UI) used by 18 ML engineers for model versioning, hyperparameter logging, and reproducibility
Designed and shipped the model registry that became the source of truth for production deployment, replacing 3 ad-hoc team workflows with a single signed-off promotion path
Promoted from Senior MLE to MLOps lead after 14 months — the fastest cross-discipline promotion in the New York ML org that year

Skills

Languages: Python, Go, Bash, SQL ML Stack: PyTorch, TensorFlow, scikit-learn, MLflow, Kubeflow, Vertex AI, Feast Infrastructure: Kubernetes, Docker, Terraform, GitHub Actions, AWS, GCP, Helm Monitoring: Prometheus, Grafana, Evidently, drift detection, model observability

Education

M.S. Computer Science 2021

Carnegie Mellon University

What makes a strong MLOps engineer resume

Lead with the production scale and the deployment story

MLOps hiring managers spend less than 10 seconds on a resume. They’re hunting for two signals: how many production models you’ve owned and how mature the deployment story is. “Owned the training-to-serving pipeline for 28 production models” tells the reader you’ve actually shipped at scale. “Worked on machine learning infrastructure” tells them nothing. Your first bullet should name the model count, the team count served, and the platform stack.

Quantify deployment velocity and the before-and-after

The single best signal of mature MLOps work is dramatically reducing model deployment time. “Cut median deployment time from 6 days to 4 hours” is the kind of bullet that makes a hiring manager think ‘this person knows the difference between a notebook and production.’ Always pair the new number with the old number — the delta is what makes the claim credible. Bonus: name the specific intervention (CI/CD pipeline, shadow eval, canary rollout) so the bullet reads as engineering work, not consulting work.

Drift monitoring is the underrated MLOps differentiator

Most ML engineers know how to train a model. Far fewer know how to detect when a deployed model has silently degraded. If you have real drift-monitoring experience — data drift, prediction drift, label drift, or concept drift — foreground it. Hiring managers reading MLOps resumes are looking for candidates who’ve operated production ML through actual failures, and drift detection is the most concrete signal that you have.

Feature store work signals platform-level thinking

Anyone can deploy a model. The MLOps engineers who get hired into staff or principal roles are the ones who’ve worked on the platform layer — feature stores, model registries, experiment tracking infrastructure, or shared training pipelines. If you’ve built or migrated any of these, surface it explicitly with the tool names (Feast, Tecton, MLflow, Weights & Biases, Kubeflow, Vertex AI). Tool fluency is the keyword recruiters search on.

Key skills for MLOps Engineer resumes

Include the ones you actually have. Leave out the ones you’d struggle to discuss in an interview.

Tools & Methodologies

Python Go Kubernetes Docker Terraform GitHub Actions AWS SageMaker Vertex AI Kubeflow MLflow Feast PyTorch TensorFlow

What Sales Hiring Managers Look For

Model Deployment CI/CD for ML Drift Monitoring Feature Stores Model Registry Experiment Tracking Shadow Eval Canary Rollouts Training Pipelines Inference Optimization

Recommended template for MLOps Engineer roles

Professional

For MLOps Engineer roles, the Professional template hits the right balance between technical credibility and platform-engineering polish. ML platform hiring managers see two failure modes on resumes: the ‘notebook engineer’ who lists every algorithm but no production work, and the ‘DevOps engineer with ML keywords sprinkled in’ who lists infra tools but no model lifecycle understanding. A clean, structured layout that puts production ML work front and center signals you’re neither.

Use this template

Frequently asked questions

What’s the difference between an MLOps engineer and an ML engineer?

MLOps engineers own the platform: training pipelines, model deployment infrastructure, monitoring, feature stores, model registries. ML engineers own the models: feature engineering, training, evaluation, inference logic. There’s overlap on every team and the titles get used interchangeably at smaller companies. At larger companies the split is real and meaningful — MLOps is a platform discipline, ML engineering is a model-building discipline. If your work is mostly ‘deploy models other people built and keep them running,’ you’re an MLOps engineer.

Do MLOps engineers need to know how to train models?

Working level, yes. You don’t need to be inventing architectures or doing original research, but you need to understand training enough to debug a pipeline that’s producing bad models. If a data scientist hands you a training script and it OOMs on your platform, you need to be able to figure out whether it’s a code issue, a data issue, or an infra issue. Pure infra-only MLOps engineers struggle in interviews when asked ‘walk me through how this model trains.’

How important is Kubernetes for MLOps roles?

Very. Most production ML platforms in 2026 run on Kubernetes (often via Kubeflow, KServe, or a managed equivalent like Vertex AI Pipelines or SageMaker). Working knowledge of Kubernetes — Pods, Services, Deployments, ConfigMaps, basic networking, Helm charts — is expected at the mid level. Deep Kubernetes expertise is a senior-level differentiator. If your background is pure Python ML and you’ve never touched Kubernetes, that’s the biggest gap to close before applying.