What is an MLOps Engineer?

MLOps Engineers bridge the gap between data science experimentation and production machine learning systems. Unlike ML Engineers who focus on model development, MLOps Engineers ensure models run reliably at scale.

The MLOps Role vs Related Roles

Role	Primary Focus	Key Skills
MLOps Engineer	Infrastructure, pipelines, automation	Kubernetes, CI/CD, monitoring
ML Engineer	Model development, training	Python, PyTorch, TensorFlow
Data Engineer	Data pipelines, storage	Spark, Airflow, SQL
Platform Engineer	Internal developer platforms	Backstage, Crossplane, GitOps

Core Responsibilities

1. Model Deployment: Containerizing models, setting up serving infrastructure
2. Pipeline Orchestration: Building automated training and inference pipelines
3. Monitoring: Implementing model drift detection, logging, alerting
4. Infrastructure: Managing Kubernetes clusters, GPU scheduling
5. CI/CD for ML: Automating testing, validation, and deployment

2026 Market Reality

The MLOps market is projected to reach $21.1 billion by 2026, with companies actively hiring for dedicated MLOps roles. Key demand drivers:

• FAANG+ companies: Google, Meta, Amazon all have dedicated MLOps teams
• AI-native startups: OpenAI, Anthropic, Cohere need production ML infrastructure
• Enterprise adoption: Banks, healthcare, retail scaling ML operations

What Interviewers Look For

When interviewing MLOps candidates, companies assess:

# Example: What interviewers expect you to know
mlops_competencies = {
    "infrastructure": ["Kubernetes", "Docker", "Terraform", "AWS/GCP/Azure"],
    "ml_pipelines": ["Kubeflow", "Airflow", "Prefect", "DVC"],
    "monitoring": ["Evidently", "MLflow", "Prometheus", "Grafana"],
    "serving": ["TensorFlow Serving", "Triton", "BentoML", "Ray Serve"],
    "cicd": ["GitHub Actions", "GitLab CI", "ArgoCD", "Jenkins"]
}

Interview Insight: Companies increasingly ask "Tell me about a time you debugged a production ML system" to assess real-world experience.

In the next lesson, we'll explore company tiers and their specific interview expectations. :::