MLOps Jobs in 2025: What Employers Actually Look For [Inside Story]

MLOps jobs are transforming the artificial intelligence landscape despite a troubling reality: less than one-third of data scientists say their models usually get deployed into production. 

This deployment gap explains why MLOps has become so crucial today. What is MLOps? It's the set of practices that ensures machine learning models are reliable, reproducible, and scalable. 

As you consider your career path, understanding what employers actually look for in MLOps engineers becomes essential. Whether you're exploring MLOps course online options or pursuing an MLOps certification, this article provides an inside look at the skills, qualifications, and experience that will make you stand out in this rapidly expanding field by 2025.

The rise of MLOps jobs in 2025

The MLOps job market has exploded into a career goldmine, transforming from a niche specialisation into one of the fastest-growing roles in the tech industry. LinkedIn's Emerging Jobs report highlights this remarkable surge, with MLOps experiencing tremendous growth in the past few years. This phenomenal expansion stems from a crucial realisation: whilst building ML models is challenging, deploying and maintaining them in production environments presents an entirely different set of obstacles.

Consequently, competition for qualified MLOps professionals has intensified. Multiple competitive offers are now commonplace for experienced candidates, sometimes with just 48-hour decision windows. Additionally, recruiters report that compensation for ML/MLOps roles has jumped significantly.

At the same time, various industries are aggressively recruiting MLOps talent. Tech giants like Google and Amazon, financial institutions such as JP Morgan, healthcare providers like GE Health, and automotive companies, including Tesla, are all expanding their MLOps teams. For professionals interested in this field, pursuing an MLOps course can significantly enhance career prospects.

What employers actually want in MLOps candidates

Beyond the impressive growth statistics, understanding what employers genuinely value in MLOps candidates is crucial for your career advancement. MLOps roles sit at the intersection of software engineering, data engineering, and machine learning, requiring a broad skill set that blends technical expertise with collaborative abilities.

Most hiring managers look for proficiency in cloud platforms (AWS, Azure, GCP), containerization tools (Docker, Kubernetes), and machine learning frameworks (TensorFlow, PyTorch). Programming skills are essential, with Python being the universal requirement, alongside familiarity with Linux and shell scripting. Moreover, knowledge of MLOps platforms like Kubeflow and MLflow has become increasingly desirable.

Interestingly, many employers emphasise that eagerness to learn new tools often outweighs mastery of specific platforms. In essence, your adaptability can be more valuable than checking every technical box. This makes obtaining an MLOps certification a worthwhile investment, even if you're still building your technical repertoire.

Equally important are cross-functional collaboration skills. The MLOps workflow involves multiple roles, including data scientists, DevOps engineers, and business stakeholders, requiring excellent communication abilities. As one industry expert put it, the ability to translate requirements and explain issues to non-experts is indispensable.

Furthermore, employers seek candidates who understand model governance fundamentals. This includes versioning of models and data, monitoring for performance degradation, and implementing automated alert systems. Experience with CI/CD pipelines specifically adapted for ML workflows is likewise highly prized.

Lastly, many job descriptions highlight the need for candidates who comprehend all phases of the ML lifecycle, from data understanding to monitoring and maintenance. Companies value professionals who can automate these processes, reducing the time required to bring models into production and ensuring their continued performance.

For career advancement, having this unique blend of software engineering, ML, and DevOps skills puts you in exceptionally high demand in the evolving landscape of MLOps jobs.

Inside real job descriptions: What top companies are asking for

To get a clearer picture of what MLOps jobs truly entail, let's peek behind the curtain at actual job descriptions from industry leaders. In practice, these postings reveal what companies value beyond the theoretical skills discussed earlier.

Examining Revolut's MLOps requirements shows how financial technology firms leverage machine learning for critical functions. Their fraud detection system, Sherlock, demonstrates the practical implementation of MLOps principles. For instance, their engineers must be proficient with Python and CatBoost for model development while understanding deployment via Flask apps on AppEngine. Interestingly, they prioritise monitoring skills using Google Cloud Stackdriver for operational performance and Kibana for functional monitoring.

Walmart's hiring approach offers another valuable perspective. The retail giant currently seeks candidates with comprehensive MLOps expertise, as evidenced by their rigorous interview process. Their Senior Machine Learning Engineer position tests candidates on core ML concepts, deep learning, generative AI, statistics, and production deployment strategies. They're also pioneering an AI-powered interview coach to help candidates prepare effectively, illustrating their commitment to finding qualified MLOps talent.

Job descriptions across the industry consistently emphasise bridging the gap between data science and operations. As one job posting states, MLOps engineers are "responsible for deploying, monitoring, and maintaining machine learning models in production environments." This requires close collaboration with data scientists, software engineers, and DevOps teams.

Indeed, salary expectations reflect the value companies place on these skills. Remote MLOps engineers with 5-9 years of experience can command competitive compensation packages, with figures varying based on experience level and location.

For those looking to enter this field, investing in an MLOps course online or pursuing an MLOps certification can substantially improve your prospects. These credentials demonstrate your understanding of essential MLOps workflows, from model development through deployment and monitoring, precisely what these job descriptions demand.

Subsequently, as companies continue refining their MLOps practices, the demand for professionals who can effectively implement end-to-end machine learning operations will only intensify.

Conclusion

As we've seen throughout this article, MLOps represents a career goldmine for professionals with the right skill set. The deployment gap between model development and implementation highlights why these roles have become so crucial. Therefore, understanding what employers actually want puts you ahead of the competition in this rapidly expanding field.

Without a doubt, the future looks bright for MLOps specialists. Companies across various industries desperately need professionals who can bridge the divide between data science and operations. This explains why salaries continue to climb and why qualified candidates often receive multiple offers with tight decision timeframes.

What makes you stand out to employers? A unique blend of technical skills and soft skills. Python programming, cloud platform expertise, and familiarity with containerization tools form your technical foundation.