MLOps Engineer
An MLOps engineer builds and maintains the systems that take machine learning models from a data scientist's laptop into production and keep them working reliably. The role sits between software engineering and machine learning. It covers the full pipeline from data ingestion, model training infrastructure, deployment, monitoring, and retraining. The ML part and the Ops part are both real requirements, not optional extras.
How It Works
MLOps engineers work across a set of specific technical problems that neither a pure software engineer nor a pure data scientist is trained to handle.
1. Building training pipelines: Raw data needs cleaning, transformation, and feature engineering before a model can train on it. MLOps engineers build pipelines that automate this process so a data scientist doesn't run scripts manually every time. Tools commonly used: Apache Airflow, Prefect, and Kubeflow Pipelines.
2. Model versioning and experiment tracking: When a data science team runs 40 experiments trying different model architectures, someone needs to track which version used which dataset and produced which accuracy. MLOps engineers set up tools like MLflow or Weights and Biases to keep this organised.
3. Deploying models to production: A trained model file is not the same as a production service. MLOps engineers wrap models in APIs, containerise them with Docker, and deploy them on Kubernetes or cloud platforms like AWS SageMaker or Google Vertex AI. A model that produces a prediction in 200ms at scale looks very different from one that works in a Jupyter notebook.
4. Monitoring and retraining: Models degrade over time as real-world data drifts from training data. An MLOps engineer sets up monitoring to catch when model accuracy drops below a threshold and triggers a retraining pipeline. Without this, a credit risk model trained in January might be making poor predictions by October with no one noticing.
How do MLOps differ from DevOps?
A DevOps engineer deploys software that behaves the same every time. An MLOps engineer deploys software whose output changes based on data. That difference creates a set of problems DevOps tools weren't built to handle, like tracking data lineage, managing feature stores, and monitoring for statistical drift.
Example
A Bengaluru health-tech startup has a data science team of 3 that built a patient readmission prediction model. The model runs in a notebook, trains manually every 2 weeks, and gets deployed by copying files to a server. They hire their first MLOps engineer. In 3 months, she automates the training pipeline, moves deployment to a containerised API on AWS, and sets up drift monitoring that alerts the team when prediction confidence drops below 80 percent. The data scientists now spend their time on model improvement rather than deployment logistics.
Common Mistakes When Hiring for This Role
1. Hiring a DevOps engineer and expecting MLOps output.
A strong DevOps engineer knows Kubernetes and CI/CD pipelines but has no experience with feature stores, model registries, or retraining triggers. The skill sets overlap but don't substitute for each other.
2. Posting the job before the ML team exists
MLOps engineers need a working ML team and real models to operationalise. Hiring MLOps before you have a data science team producing models results in an expensive engineer with nothing to deploy.
FAQs
1. Do I need a machine learning background to become an MLOps engineer?
You need enough ML knowledge to understand what models need in production. Most MLOps engineers come from software or data engineering and learn ML concepts on the job. A pure ML researcher background without engineering depth is a harder path.
2. What skills should an MLOps engineer know?
Python, Docker, Kubernetes, at least one cloud platform, and familiarity with tools like MLflow, Airflow, and either SageMaker or Vertex AI. SQL and a basic understanding of model evaluation metrics also matter.
3. Is MLOps the same as DataOps?
No, DataOps covers data pipelines and data quality for analytics and reporting. MLOps specifically covers the pipeline from model training to production deployment and monitoring. They overlap in the data layer but diverge after that.
4. What does an MLOps engineer earn in India?
At mid-level with 3 to 5 years of experience, MLOps engineers in Bengaluru and Hyderabad earn between ₹18 and ₹35 LPA depending on company stage and stack depth. Senior MLOps engineers at funded AI startups earn higher.
5. How is this role different from an ML engineer?
An ML engineer typically builds and trains models. An MLOps engineer focuses on the infrastructure that runs those models in production. Some companies combine both into one role at an early stage. Larger teams keep them separate.
