A template for MLOps engineers who get models off the laptop and into reliable production.
MLOps engineers turn trained models into reliable production systems. They build the pipelines that train, package, deploy, and monitor models, automate retraining, watch for drift, and keep latency and cost under control. The role sits between data science and platform engineering, and it owns the whole path from notebook to served endpoint. A normal week mixes CI/CD for models, containerisation with Docker and Kubernetes, monitoring dashboards, and the occasional 2am page when a model quietly degrades. Hiring managers in 2026 care less about how clever the model is and more about whether it stays up, stays cheap, and stays accurate after launch. This page gives you a CV example, real Western salary numbers, and the interview answers that show you think in production terms, not notebook terms.
MLOps Engineer with 7 years bridging machine learning and platform engineering. Built a deployment platform serving 30 plus models with automated retraining and drift monitoring at 99.9 percent uptime, and cut inference cost by roughly 40 percent through right-sizing, batching, and caching. Fluent across Python, Kubernetes, MLflow, and AWS, with a track record of taking notebook prototypes all the way to reliable production endpoints.
Production scale and reliability are the first things a recruiter scans for. "Built a platform serving 30 plus models with automated retraining and drift monitoring at 99.9 percent uptime" beats "worked on machine learning" every time. They want to see that you own the path from a data scientist's notebook to a served endpoint, and that you've handled the boring parts: versioning, rollbacks, on-call, and cost. Name the cloud and the orchestration tools you actually used in production, not the ones you read about.
List model deployment, CI/CD for ML, Docker, and Kubernetes near the top because those are the screen filters. Add an experiment and model registry tool like MLflow or Kubeflow, monitoring and drift detection, strong Python, and a cloud platform such as AWS SageMaker or GCP Vertex AI. Infrastructure-as-code with Terraform is a clear plus. Show one or two of these tied to a result, for example "cut inference cost 40 percent with right-sizing and batching" rather than a flat tool list.
The biggest one is submitting a data-science CV with no production or infrastructure depth. MLOps is engineering first, modelling second, and a wall of model accuracy numbers without a single deployment or uptime figure reads as the wrong role. Other misses: listing 20 tools with no context, hiding the impressive scale number in paragraph four, and forgetting to mention on-call or incident work, which is exactly what separates this role from pure research.
Keep it to one or two pages. Put a two-line summary at the top with your headline number, such as models in production and uptime. Use a clean single-column layout that parses well in applicant tracking systems, and skip the photo for US and UK applications. Lead each role with the platform you built and its scale, then list achievements as bullets with numbers. Save fonts and colour for the design, not the structure.
Figures in USD. Ranges reflect mid-level experience (3–7 years). Senior roles and major metro areas typically sit at the top of these bands.
Tech firms and larger enterprises want proof you can deploy and run models at scale, not just train them. Lead with the number of models you've put in production, the platform you built, and your uptime. Name your stack clearly: Docker, Kubernetes, MLflow or Kubeflow, plus a cloud ML service like SageMaker or Vertex AI. Reliability and cost wins read as engineering maturity, so put uptime, drift monitoring, and cost-per-prediction near the top. A real CKA or AWS Machine Learning certificate helps you clear automated screens.
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