The Complete Guide to Becoming an MLOps Engineer in 2025
Learn the skills, salary insights, and career roadmap you need to land a high-paying MLOps role.
AI is everywhere. From chatbots handling customer service to recommendation engines curating your Netflix binge sessions, machine learning is transforming industries. But here’s the thing—building an AI model is just one part of the equation. Getting that model to run efficiently in the real world, making sure it scales, stays accurate, and doesn’t break is a whole different challenge.
That’s where MLOps Engineers come in.
If you’re wondering what it takes to become one, how much you can earn, and how to get started—keep reading. This might just be one of the best-paying career moves you make.
Kelechi Edeh
Who is an MLOps Engineer?
Imagine you’re a chef. You create an incredible dish, but the real challenge is making sure every customer in a packed restaurant gets the same high-quality meal—without waiting hours. That’s exactly what an MLOps Engineer does, but instead of food, they handle machine learning models in production.
While data scientists and ML engineers focus on developing AI models, MLOps engineers ensure those models actually work in real-world applications. They manage the infrastructure, automate processes, monitor performance, and make sure AI doesn’t just sit on a research paper but delivers value to millions of users.
Simply put, an MLOps Engineer is the DevOps expert of AI—ensuring that machine learning models don’t just work but keep working at scale.
Oyinebiladou Omemu
How Much Does an MLOps Engineer Earn?
MLOps sits at the intersection of machine learning, cloud computing, and DevOps—which means salaries are on the higher end of the tech spectrum. Per data Glassdoor data, here's what MLOps Engineers in Nigeria and India earn.
Nigeria
- Entry-level: ₦1.8M – ₦3.6M per year (~$1,170 – $2,300)
- Mid-level: ₦3M per year (~$1,930)
- Senior-level: ₦4M+ per year (~$2,400)
- Top Employers: Andela, Interswitch, Flutterwave, Kuda, Paystack
India
- Entry-level: ₹6L – ₹12.3L per year (~$6,900 – $14,100)
- Mid-level: ₹9L – ₹20.5L per year (~$10,300 – $23,600)
- Senior-level: ₹30L+ per year (~$34,600+)
- Top Employers: TCS, Infosys, Swiggy, Flipkart, Wipro, Razorpay
AI-driven industries, especially fintech, cloud computing, and AI startups, pay the most for MLOps expertise since they rely on machine learning models for critical business operations.
Role of an MLOps Engineer
So, what does an MLOps Engineer actually do? Well, if you think they’re just tweaking AI models all day, think again. The job is part software engineering, part DevOps, and part machine learning operations.
Most days, you’ll find yourself:
- Deploying and automating AI models
- Managing cloud infrastructure
- Monitoring model performance
- Making sure data pipelines run smoothly.
You’ll be responsible for handling model versioning, retraining strategies, and ensuring that AI systems don’t go rogue (because no one wants their AI chatbot to suddenly start spewing nonsense).
A big part of the job is also optimizing costs—because running AI models at scale isn’t cheap. Whether it’s tuning GPU usage for efficiency or automating workflows, your job is to make sure AI runs fast, efficiently, and without breaking the bank.
Skills Needed to Become an MLOps Engineer
MLOps is a hybrid role, meaning you need to master both machine learning fundamentals and DevOps practices.
Technical skills
- Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn
- Cloud Computing: AWS, Google Cloud, Azure
- Containerization & Orchestration: Docker, Kubernetes
- DevOps & CI/CD: Jenkins, GitHub Actions, Terraform
- Data Engineering: Apache Spark, Airflow, Kafka
- Monitoring & Logging: Prometheus, Grafana, MLflow
Soft skills
- Problem-solving mindset—AI models can break unexpectedly, and you need to be the one fixing them fast.
- Collaboration—you’ll work with data scientists, engineers, and business teams to keep AI operations running smoothly.
- Communication—explaining technical concepts to non-technical stakeholders is part of the gig.
Roadmap to Becoming an MLOps Engineer
Here's how to get started:
/1. Start with machine learning basics
Before getting into MLOps, you need to understand how machine learning models work. Start by learning about supervised vs. unsupervised learning, model training, hyperparameter tuning, and how AI models make predictions.
You don’t need to be a data scientist, but if you don’t know what a confusion matrix is, it’s time to hit the books. Platforms like Coursera, Udacity, and YouTube tutorials can help you get started.
/2. Get comfortable with DevOps & cloud platforms
Since most machine learning models run on AWS, Google Cloud, or Azure, learning cloud computing and DevOps is non-negotiable. Start by understanding cloud storage, networking, and security best practices.
At the same time, get hands-on experience with Docker and Kubernetes, as they’re essential for deploying AI models efficiently. A great hands-on project could be deploying a small AI model using AWS Lambda or Google Cloud Functions.
/3. Master CI/CD for machine learning
Unlike traditional software, AI models need frequent updates to stay accurate. That’s why continuous integration and continuous deployment (CI/CD) pipelines are a must in MLOps.
Learn how to set up automated workflows using Jenkins, GitHub Actions, and Terraform to retrain and redeploy models whenever new data is available. A good way to practice is to set up a GitHub Actions pipeline that retrains and deploys an AI model automatically.
/4. Gain experience in model monitoring & optimization
Machine learning models degrade over time (a problem known as model drift), so monitoring their performance is critical.
Learn how to use tools like Prometheus, Grafana, and MLflow to track model performance, detect issues early, and optimize cost-efficiency. For a simple project you could create a dashboard that tracks how well an AI model performs over time.
/5. Consider earning an MLOps certification
While not mandatory, certifications help you stand out in the job market. Some solid choices include:
- AWS Certified Machine Learning – Specialty
- Google Professional ML Engineer
- Microsoft Certified: Azure AI Engineer
If you’re switching from software engineering or DevOps, a certification can boost your credibility in AI-related roles.
/6. Build a portfolio & apply for jobs
The best way to land a job is to show your work.
So, set up a GitHub portfolio with:
- A CI/CD pipeline that automates AI model deployment.
- A Dockerized AI model running on Kubernetes.
- A real-time AI monitoring setup using Grafana & Prometheus.
Then, start applying for MLOps roles—especially in fintech, cloud computing, and AI startups, where demand is booming.
Conclusion
AI isn’t slowing down anytime soon, and MLOps Engineers are the backbone of real-world machine learning applications. If you’re looking for a high-paying, future-proof career, this is it.
The journey won’t be easy, but if you start learning today, build hands-on projects, and apply for jobs, you’ll be well on your way to becoming an MLOps Engineer in 2025.
