Hiring MLOps Engineers: The Complete Guide

What MLOps Engineers Actually Do

MLOps Engineers are responsible for the infrastructure and tooling that enables data scientists and ML engineers to do their work efficiently and reliably.

A Day in the Life

ML Platform Development (Core Responsibility)

Building internal platforms that accelerate ML development across the organization:

• Training infrastructure — GPU clusters, distributed training setups, experiment orchestration (Kubeflow, Metaflow)
• Feature platforms — Feature stores (Feast, Tecton), feature engineering pipelines, feature versioning
• Experiment tracking — MLflow, Weights & Biases, or custom solutions for tracking experiments and artifacts
• Model registry — Centralized model storage, versioning, lineage tracking, approval workflows
• Data pipelines — ETL/ELT for ML, data quality checks, schema validation, drift detection

CI/CD for Machine Learning

ML CI/CD is fundamentally different from traditional software—MLOps Engineers design these specialized pipelines:

• Model testing — Unit tests for preprocessing, integration tests for pipelines, model validation gates
• Data testing — Schema validation, distribution checks, drift detection, data quality monitoring
• Automated retraining — Trigger-based retraining pipelines, champion-challenger deployment patterns
• Reproducibility — Environment management, dependency versioning, seed control, artifact immutability
• Deployment automation — Canary releases for models, A/B testing infrastructure, rollback mechanisms

Infrastructure & Cost Management

ML workloads are expensive and resource-intensive:

• Compute optimization — GPU utilization, spot instance management, cluster autoscaling
• Cost monitoring — Training cost attribution, serving cost analysis, optimization recommendations
• Resource scheduling — Fair scheduling across teams, priority queues, preemption policies
• Multi-cloud strategy — Cloud provider selection, hybrid deployments, avoiding vendor lock-in

MLOps vs. ML Engineer vs. Data Engineer

Understanding the distinction prevents hiring mistakes:

MLOps Engineer

• Focus: ML infrastructure, platforms, pipelines
• Builds: Training platforms, feature stores, model registries
• Success metrics: Platform reliability, developer productivity, cost efficiency
• Reports to: Platform/Infrastructure team or ML leadership

ML Engineer

• Focus: Model deployment, serving, production ML systems
• Builds: Prediction APIs, inference optimization, model monitoring
• Success metrics: Model latency, uptime, prediction quality
• Reports to: Product team or ML team

Data Engineer

• Focus: Data infrastructure, warehousing, ETL
• Builds: Data pipelines, warehouses, streaming systems
• Success metrics: Data quality, pipeline reliability, query performance
• Reports to: Data team or Engineering

The overlap: All three roles touch data pipelines. MLOps builds ML-specific pipelines (training data, features). Data Engineers build general data infrastructure. ML Engineers consume both to serve models.

Skill Levels: What to Expect

Career Progression

Junior MLOps Engineer (0-2 years)

• Maintains existing ML pipelines and infrastructure
• Writes basic Airflow DAGs and Kubernetes manifests
• Monitors ML platform health using existing dashboards
• Debugs pipeline failures with guidance
• Documents processes and runbooks

Mid-Level MLOps Engineer (2-5 years)

• Designs ML pipelines for new use cases
• Implements feature engineering platforms
• Optimizes training costs and GPU utilization
• Handles production incidents independently
• Evaluates and integrates new MLOps tools
• Mentors juniors on infrastructure practices

Senior MLOps Engineer (5+ years)

• Architects ML platforms that scale across teams
• Drives build vs. buy decisions for ML infrastructure
• Sets MLOps standards and best practices
• Influences vendor selection and technical roadmap
• Collaborates with leadership on ML strategy
• Handles complex, cross-team technical challenges

The MLOps Stack: What to Evaluate

Data & Feature Layer

• Feature stores: Feast, Tecton, Hopsworks, or custom solutions
• Data versioning: DVC, Delta Lake, lakeFS
• Data quality: Great Expectations, dbt tests, Monte Carlo

Training & Experimentation

• Orchestration: Kubeflow, Metaflow, Prefect, Dagster
• Experiment tracking: MLflow, Weights & Biases, Neptune
• Distributed training: Ray, Horovod, SageMaker

Model Management

• Model registry: MLflow, Vertex AI, SageMaker
• Model serving: Seldon, KServe, TensorFlow Serving, Triton
• Monitoring: Evidently, Fiddler, WhyLabs

Infrastructure

• Compute: Kubernetes, Kubeflow, cloud GPU services
• Cost management: Kubecost, cloud cost tools
• Observability: Prometheus, Grafana, DataDog

Interview Framework

Technical Assessment Areas

1. Infrastructure design — "Design an ML training platform for 50 data scientists"
2. Pipeline debugging — "A training job that worked yesterday now fails—walk through debugging"
3. Feature engineering — "How would you ensure feature consistency between training and serving?"
4. Cost optimization — "Training costs increased 3x last quarter. How do you investigate?"
5. Drift detection — "How do you detect and respond to data drift in production?"

Red Flags

• Can't explain why feature stores matter
• No experience with GPU workloads or distributed training
• Treats ML infrastructure like regular infrastructure
• Doesn't understand reproducibility challenges
• Never dealt with model versioning or lineage

Green Flags

• Has war stories about ML pipeline failures
• Understands the full ML lifecycle
• Can discuss trade-offs between MLOps tools
• Experience with feature engineering at scale
• Proactively thinks about cost and reproducibility

Market Compensation (2026)

Premium areas: Feature store experience, Kubernetes/GPU expertise, FAANG ML platform experience.

When to Hire MLOps Engineers

Signals You Need MLOps

• Data scientists waiting days for training jobs
• ML models failing in production with no clear diagnosis
• No reproducibility—can't recreate last month's model
• Feature engineering duplicated across projects
• Training costs growing faster than models

Team Size Guidelines

• 1-3 ML practitioners: DevOps can handle basics, maybe 1 MLOps
• 4-10 ML practitioners: 1-2 dedicated MLOps Engineers
• 10+ ML practitioners: MLOps team with platform specializations

Alternative Approaches

• Managed services: Vertex AI, SageMaker can defer MLOps hiring
• Platform companies: Weights & Biases, Tecton reduce custom work
• ML Engineers stretch: Senior ML Engineers can cover basics initially