How to become an MLOps engineer in 2026

Machine Learning Operations (MLOps) is an excellent field for anyone interested in a career switch into AI, or for those just beginning their career as a technologist. MLOps Engineers are skilled professionals that empower AI practitioners (i.e., Data Scientists, Machine Learning Engineers, etc.) and teams with better tooling and support. It is a role that every DS/ML/AI team should consider adding to their roster.

I’ve worked as an MLOps engineer across multiple organizations and industries over the past four years. I’ve also spent time learning from peers through meetups and networking events, witnessing firsthand what it’s like to work on the front lines of this rapidly evolving field. In this article, I’ll walk you through fundamentals and practical steps that you can take today to start a career in MLOps.

What is MLOps? And why is it relevant?

MLOps is a DevOps specialty within the expanding field of Artificial Intelligence. The discipline incorporates important practices like iterative development, versioning and reproducibility, automated deployments, and ongoing monitoring across the ML lifecycle. Though titles may vary, the term “MLOps Engineer” has become common in job postings today as organizations seek better reliability and performance out of their ML systems.

The role of an MLOps Engineer typically resembles that of a DevOps Engineer, but one that has the necessary acumen to build and support ML-enabled applications and services. As an MLOps Engineer, I often would work closely with Data Scientists, Machine Learning Engineers, and adjacent teams like architecture and cybersecurity. Together we focused on delivering repeatable and standardized approaches to (1) take ML models from conception to production as quickly as possible, (2) improve experimentation, development, and deployment workflows, and (3) reduce risk while increasing the quality and visibility of our ML systems.

What skills does an MLOps Engineer need?

As noted in Pluralsight’s 2026 Tech Forecast, “data scientists are shifting to think like systems engineers” in delivering their work. This is exactly from where the role of an MLOps Engineer came to be. 

In many ways, MLOps is “systems engineering” for Data Science and Machine Learning. Kind of like stacking lego blocks together to form a whole, MLOps connects various systems and processes together for a desired outcome (i.e., train/deploy/serve an ML model). The following image illustrates this well as it shows several areas where MLOps can add value in the process of training, deploying, and serving ML models:

See MLOps Stack Canvas by ml-ops.org

You’ll notice that there isn’t any specific technology or skill mentioned in that diagram, but rather high-level components for delivering robust ML systems. I find this to be a useful abstraction because it allows individuals to insert the technologies and skills they might already be familiar with. 

For example, you might already be a skilled engineer that can look at that picture and envision tools in your toolkit that you’d use for things like a “code repository” or “application monitoring”. If so, that’s great, and this is exactly how you should approach becoming an MLOps Engineer! MLOps is a language-, framework-, platform-, and infrastructure-agnostic discipline where you should feel empowered to use the tools that make the most sense for you.  

However, there are most likely some skill gaps that you see in yourself when looking at that diagram. That’s okay, and is to be expected! It’s a lot to know and generally takes an entire team to completely cover. That being the case, here are popular technologies that I would recommend you consider investing in as you begin your MLOps journey:

• AWS

• Linux

• Python

• Git

• Docker

• Terraform

• Ansible

• GitHub Actions

• Computer Networking

When picking up a new skill, I always prefer those that are evergreen and can be used in many different roles. The above list of skills are exactly that and are also commonly used in MLOps. My recommendation is to start learning whichever tool makes the most sense now for yourself.

While you do so, please consider taking Pluralsight Skill IQ assessments as you go along so that you can track your progress and receive tailored recommendations on where to focus your time and effort. As a reference, check out my blog post on how I learned AWS using Pluralsight, and the data I was able to capture over time using Skill IQ assessments.

How to get a job as an MLOps Engineer in today’s market

It’s no secret that the job market has been challenging these past few years. Both entry-level and experienced professionals are finding it harder to land roles that feel stable, well-scoped, and rewarding. That said, MLOps Engineer roles are in high demand and can be an excellent pivot for anyone looking to stay relevant as organizations continue to invest in AI. Below are a few practical strategies that can meaningfully improve your chances on the job hunt.

1. Start where you are (If you’re currently employed)

If you’re already working in a technical role, one of the best moves you can make is to advocate for an investment in MLOps internally. Many organizations already have AI initiatives that struggle due to a lack of proficiency in deployment, monitoring, and/or governance practices. Volunteering to help solve these problems could give you real-world experience while reducing career risk. Not only that, it might open up a new dream gig for yourself!

2. Use LinkedIn intentionally

I’ve found that LinkedIn remains the most effective platform for job discovery. My advice is to keep your profile active and up-to-date, share what you’re learning or building, and engage with MLOps-related posts and discussions

Turn on job alerts and watch for roles with the following job titles: MLOps Engineer, ML Platform Engineer, or AIOps Engineer. Titles can vary widely, but the underlying responsibilities often overlap.

3. Build hands-on experience (This is non-negotiable)

Hands-on experience is absolutely essential. If possible, try to incorporate MLOps practices into your own projects. Leverage the AWS free tier to build and deploy real systems with a goal to operate in an environment that resembles “production” as closely as possible.

As you build, make sure you have a GitHub profile that can effectively share your story and progress with others. Build personal projects that showcase how you’ve applied MLOps concepts end-to-end. Document the decisions and tradeoffs you made along the way.

Practice using new skills and technologies by using structured learning platforms and hands-on labs like Pluralsight. Not only that, I highly recommend pursuing relevant certifications, like the AWS Machine Learning Specialty, as a way to hold yourself accountable during your learning and to capture a credential along the way! 

4. Engage with the community

Community involvement can also accelerate your learning and open doors. Many cities have free or low-cost MLOps, ML/AI, or cloud meetups. These are excellent places to learn, network, and sanity-check your career direction. There are also many online forums and communities, like r/mlops, that can help you passively absorb real-world challenges, tooling discussions, and hiring trends. If you feel up for it, you could even consider contributing to open-source projects like MLflow to go the extra mile and showcase your talent and give back to the community.

A word of caution

One last tip: Beware of roles where everything seems to be crammed into one job. It’s common for startups to post a role that is somehow in charge of data science, software engineering, DevOps, security, on-call support, etc. Roles like these are a recipe for personal burnout and organizational failure. Ignore these, even if it means prolonging your job hunt. Your personal life and well-being matter – Don’t accept a role if you feel either will be compromised.

Conclusion

Feel free to reach out to me on Linkedin if you have any questions. If you liked this article, check out some of my other pieces with Pluralsight:

• Training Models with Amazon SageMaker

• Cleaning and Working with Dataframes in Python

• Data Storytelling

• AI technology: The make or buy decisions

I have another course in the works right now that will be very applicable to you and your MLOps journey. Join Pluralsight with a free trial to follow along!