Table of Contents
MLOps isn't just DevOps for ML. It adds complexity: data versioning, model drift, experiment tracking, and GPU scheduling. Here's what to look for when hiring an MLOps engineer.
Core Pillars of MLOps
Must-have expertise:
- ✦ CI/CD for ML (code → model → deployment pipeline)
- ✦ Data pipelines (training data, feature engineering, inference data)
- ✦ Model monitoring (drift, performance, data quality)
- ✦ Model registry and versioning
- ✦ Infrastructure (GPU clusters, auto-scaling, spot instances)
- ✦ Governance and compliance (audit trails, reproducibility)
Tooling Experience
Look for familiarity with:
- ✦ Experiment tracking: MLflow, Weights & Biases, Neptune
- ✦ Model serving: TensorFlow Serving, TorchServe, BentoML, KServe
- ✦ Workflow orchestration: Kubeflow, Airflow, Prefect, Dagster
- ✦ Feature stores: Feast, Tecton, Databricks Feature Store
- ✦ Monitoring: Evidently AI, WhyLabs, Arize, Fiddler
Artifacts a Strong MLOps Engineer Produces
- ✦ Reusable CI/CD templates
- ✦ Model registry standards
- ✦ Monitoring dashboards
- ✦ Incident response runbooks
- ✦ Data validation pipelines
- ✦ Automated retraining workflows
How MLOps Differs from DevOps
Artifact
DevOps:
Code + binaries
MLOps:
Code + data + models
Versioning
DevOps:
Git
MLOps:
Git + DVC + model registry
Testing
DevOps:
Unit + integration tests
MLOps:
Plus data validation, model evaluation
Rollback
DevOps:
Revert code
MLOps:
Revert code AND model version
Monitoring
DevOps:
CPU, memory, latency
MLOps:
Plus data drift, concept drift, model performance
MLOps requires everything DevOps does, plus ML-specific complexity.
Interview Questions
Automated data validation, model training, evaluation against baseline. If passes, package model, deploy to staging, run shadow tests, then canary deployment to production.
Data drift: input distribution changes. Concept drift: relationship between input and output changes. Detect both; remediation differs.
MLOps Hiring Scorecard
Infrastructure
Weight:
25%
Model Deployment
Weight:
25%
Monitoring
Weight:
20%
Automation
Weight:
20%
Governance
Weight:
10%
Find True MLOps Expertise
MLOps engineers are rare. They combine software engineering, data engineering, and ML knowledge. Offline Pixel pre-vets MLOps expertise before you interview. Raise a request, talk to candidates, fund the project, and approve payment when the work is done.
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