MLOps engineer: complete hiring guide with job description

Only 13% of AI projects actually move from proof-of-concept to production. Meanwhile, 88% of organizations deploy AI in at least one function. That gap between experimentation and production is staggering.

The problem is not the algorithms. Data scientists build models that work brilliantly in notebooks. The problem is getting those models into production and keeping them running.

That’s where MLOps engineers come in. But most companies write the mlops engineer job description completely wrong.

Why most MLOps hiring fails

Companies post jobs asking for machine learning expertise, deep learning knowledge, and advanced statistics. Then they wonder why their new hire cannot get models deployed.

Research shows most MLOps engineers come from software development backgrounds, not data science. The role demands someone who can manage servers, build CI/CD pipelines, and debug production systems. Understanding gradient descent is secondary. And MLOps skills are now treated as minimum requirements rather than differentiators in AI hiring.

I keep seeing this pattern. A company hires a talented data scientist, slaps an MLOps title on them, and expects magic. Six months later, models are still running on someone’s laptop because nobody knows how to containerize them or set up monitoring.

The math is straightforward. Your MLOps engineer will spend most of their time on infrastructure, deployment automation, monitoring, and incident response. Maybe 30% of their work touches machine learning concepts directly.

What MLOps engineers actually do

Let me break down where the time goes.

Infrastructure management takes up the biggest chunk. Your MLOps engineer designs and maintains the systems that host ML models. This means cloud platforms like AWS, Azure, or GCP. Container orchestration with Kubernetes. Setting up compute resources that can handle inference at scale.

Over 60% of enterprise AI initiatives fail to scale without dedicated operational support, and monitoring is a big reason why. Production ML systems fail in ways traditional software does not. Data drifts. Model performance degrades. Input distributions shift. Your MLOps engineer builds systems to catch these issues before they tank your business metrics.

Deployment automation matters more than model sophistication. Google’s MLOps documentation emphasizes CI/CD pipelines that automatically test, validate, and deploy models. Your MLOps engineer creates these pipelines so data scientists can focus on improving models instead of figuring out deployment.

Incident response happens weekly in production ML systems. Models break. APIs time out. Dependencies conflict. Your MLOps engineer needs to debug these issues fast, often at 2 AM.

The DevOps foundation you need

Start with someone who can build and maintain production systems.

Your ideal candidate has strong experience with Docker and Kubernetes. Current job posting data shows Kubernetes appears in 17.6% and Docker in 15.4% of MLOps listings, confirming these as non-negotiable skills. They have built CI/CD pipelines before, probably using Jenkins, GitLab CI, or GitHub Actions. They understand monitoring and logging with tools like Prometheus, Grafana, or DataDog.

Python proficiency matters more than knowing obscure ML algorithms. Your MLOps engineer needs to write clean, maintainable code that other engineers can work with.

Cloud platform expertise is non-negotiable. Most ML workloads run on AWS, Azure, or GCP. Your candidate should know how to provision resources, manage costs, and design for reliability across regions.

The ML knowledge can be basic. They need to understand what models do, how training differs from inference, and why data quality matters. They don’t need to derive backpropagation from first principles.

Here’s what sets MLOps engineers apart from data scientists.

Automation thinking - Everything should run without manual intervention. Model retraining, data validation, deployment, rollback. If a human has to click buttons, the system is not production-ready.

Systems thinking - ML models are components in larger systems. Your MLOps engineer sees the whole picture: data pipelines, feature stores, model serving, monitoring, and feedback loops.

Reliability focus - McKinsey’s 2025 data shows less than one-third of organizations follow most adoption and scaling best practices. Your MLOps engineer designs for uptime, graceful degradation, and quick recovery when things break.

Cost awareness - ML infrastructure gets expensive fast. GPU instances, data storage, API calls. Your MLOps engineer optimizes costs without sacrificing performance.

Writing an effective mlops engineer job description

Most job descriptions read like wishlists. They ask for every possible skill and scare away qualified candidates who do not check every box.

Be honest about what you actually need.

Required skills:

• Strong DevOps experience with production systems
• Proficiency in Python and one cloud platform
• Experience with Docker and container orchestration
• Understanding of CI/CD principles and implementation
• Monitoring and logging experience
• Basic ML concepts and workflows

Nice to have:

• Direct MLOps experience
• Multiple cloud platforms
• Familiarity with ML frameworks like TensorFlow or PyTorch
• Experience with specific ML tools like MLflow or Kubeflow
• Data engineering background

Notice the order. DevOps skills come first. ML expertise is secondary.

Your job description should emphasize real responsibilities: building deployment pipelines, maintaining production systems, creating monitoring dashboards, responding to incidents. Not “developing cutting-edge algorithms” or “advancing the state of the art.”

The best mlops engineer job description I have seen focused entirely on production challenges and said almost nothing about research.

Test for the skills that matter.

Give candidates a production scenario. A model is deployed but performing poorly. How do they diagnose the issue? What metrics do they check? How do they roll back safely?

Ask about their infrastructure experience. Have them walk through a deployment pipeline they built. How did they handle version control for models? What monitoring did they implement?

Skip the whiteboard algorithm questions. You are not hiring a research scientist. You need someone who can take a working model and run it reliably at scale.

AI specialist jobs are growing 3.5x faster than all jobs, and AI/ML roles saw 88% year-on-year growth in hiring in 2025. Demand is exploding. But the talent pool stays small because companies keep looking for unicorns instead of hiring DevOps engineers and teaching them ML basics. Workers with AI skills now command a 56% wage premium according to PwC, up from 25% the prior year.

The reality of building ML systems

Most AI project failures are not algorithm failures. Only 6% of organizations are high performers reporting meaningful EBIT from AI, while 87% of tech leaders face challenges finding skilled workers to close the gap.

The failures happen in production. Infrastructure cannot scale. Monitoring is absent. Deployment takes weeks. Models drift and nobody notices until customers complain. By 2026, the IT skills shortage is expected to cost trillions in losses according to IDC.

Your MLOps engineer prevents these failures. But only if you hire for production skills first.

Stop looking for people who can do everything. Hire a strong DevOps engineer who is curious about machine learning. Teach them the ML concepts they need. Give them time to learn how models work and what makes them special. The IMF reports that 39% of today’s skills will become outdated by 2030, and skill demands are changing 66% faster in AI-exposed roles. Building on a strong DevOps foundation gives your hire the adaptability to evolve with the field.

That approach works better than hiring a brilliant data scientist and hoping they figure out Kubernetes.

The MLOps engineer role exists because getting models to production is hard. Not intellectually hard in the way proving theorems is hard. Operationally hard in the way running reliable systems at scale is hard.

Hire for operational skills. The ML part is easier to teach than you think.