As it becomes increasingly difficult to separate software engineering from artificial intelligence (AI), the need for AI-trained software professionals grows more evident. Organizations that deploy and maintain AI systems at scale are turning to knowledgeable and skilled machine learning operations (MLOps) engineers to keep models working reliably through all stages of production.
At Boston University (BU), the online Master of Science (MS) in Software Engineering for Artificial Intelligence degree program features a career-centered curriculum with coursework that builds the competencies MLOps engineers need in their everyday work — from pipeline design and deployment to monitoring, scalability, and responsible operation.
Read on to learn more about what a career in MLOps engineering entails, the types of roles available, and how the MS in Software Engineering for AI aligns with essential MLOps skills.
What Is an MLOps Engineer?
For those already familiar with software development and engineering, it can be helpful to think of MLOps engineering as the machine learning (ML) equivalent of DevOps.
In many ways, MLOps engineers aim to merge gaps between software engineering, data engineering, and AI operations — particularly within organizations that work extensively with AI system deployment and monitoring. More specifically, these professionals help operationalize ML systems by building and managing the pipelines, infrastructure, workflows, and monitoring required to keep models working reliably at every stage of production.
What Does an MLOps Engineer Do?
The particular responsibilities of an MLOps engineer can vary based on the given position, but all duties revolve around supporting the lifecycle of AI models throughout the production process. Examples include:
- Building and managing production pipelines – This involves designing the data and model pipelines that allow AI systems to move from development into repeatable production workflows. (BU’s MS in Software Engineering for AI program includes specific coursework on designing scalable data pipelines and MLOps workflows for this purpose.)
- Monitoring performance and supporting reliability – Even after a system is deployed, the job of the MLOps engineer isn’t complete. These professionals continuously monitor system behavior and performance to catch issues, support iteration, and maintain stable model performance over time.
- Connecting models to real software systems – Because models need to be integrated into production applications and larger software systems, MLOps engineers must be able to work with models in the context of real environments.
Why MLOps Engineering Matters in the AI Era
Today, many organizations are experimenting with AI models in some regard. However, building and testing models is one undertaking — but running them reliably, securely, and at scale is another. MLOps engineers enable organizations to make AI modeling a more sustainable business asset while bridging gaps between software engineering, data engineering, and AI teams.
The Gap Between Model Development and Real-World Deployment
Without MLOps engineering experience, teams may encounter challenges when it comes to deploying models in a way that teams can trust, scale, and maintain. MLOps engineers can be there to step in at the critical point between training and deployment, moving projects past the experimental phase to enhance reliability and accuracy.
Production AI Requires Operational Discipline
Once AI becomes part of modern software systems, it also demands engineering discipline around infrastructure, scalability, release workflows, and maintenance. This is where the operational discipline of the MLOps engineer stands out, as these professionals fill vital skill gaps and work to prevent model degradation.
Where MLOps Engineers Fit on AI Teams
In the context of larger AI/ML teams, MLOps engineers regularly collaborate with data scientists and AI software engineers to bridge infrastructure, applications, and operations.
Working Alongside Data Scientists and AI Software Engineers
Whereas data scientists may focus more on model development, for example, MLOps engineers step in to ensure these systems are deployed and running reliably. Likewise, while AI software engineers work to build a running model, MLOps engineers are the ones making sure that the model is yielding dependable results.
Bridging Infrastructure, Applications, and Operations
Ultimately, MLOps engineers often sit between model work and software operations, bridging gaps by building sustainable production systems that make AI a usable, sustainable asset in contemporary organizations.
Common Types of MLOps Engineer Jobs
Job titles in MLOps engineering can vary, but the work generally falls into a few recognizable categories:
- MLOps/AI platform engineer roles focus on deploying, monitoring, and maintaining AI systems, pipelines, and infrastructure.
- AI infrastructure and deployment jobs center on release workflows, model-serving environments, and operational support for AI-enabled applications.
- Production AI and reliability-focused engineering positions prioritize keeping AI systems available, scalable, and maintainable after deployment (rather than designing the models themselves).
What the Role Looks Like in Practice
It’s one thing to think about what MLOps engineering looks like in an abstract sense. Those considering this as an actual career path, however, will want to have a firmer grasp of what the day-to-day work may look like.
Supporting Reliable Model Deployment
Daily work in MLOps engineering may entail helping to move AI models into production environments using repeatable workflows and operational safeguards. This might include using tools to automate training and deployment while continuously monitoring model performance for signs of problems.
Designing Systems That Can Scale
Another central aspect of any MLOps engineer’s job is helping teams design scalable systems that can be used reliably. This part of the job warrants a deep understanding of:
- Throughput
- Data flow
- Architecture
- How AI behaves under real usage conditions
Improving AI Systems Over Time
MLOps engineers don’t consider their work complete when systems are deployed at scale; instead, they’re always seeking ways to improve by supporting iteration, updates, and long-term systems health. This may mean:
- Tracking newly deployed systems for signs of drift or accuracy issues
- Automating testing of new models
- Working alongside other teams to standardize deployment
How To Become an MLOps Engineer
There is no “one-size-fits-all” path to becoming an MLOps engineer. Still, you can follow some steps to establish the practical skills and understanding needed in this line of work.
Build a Foundation in Software, Data, and Production Systems
Many people move into MLOps from fields like software engineering, data engineering, ML engineering, or related technical roles. Possessing a solid foundation in these areas can help prepare you for the challenges and opportunities of MLOps engineering, so sharpening your knowledge of software fundamentals and AI deployment is crucial.
Gain Experience with Real AI Operations Problems
Experience with real AI operations pipelines, deployment, monitoring, and production can go a long way in MLOps engineering roles. A graduate-level education, like Boston University’s MS in Software Engineering for AI, can present an optimal pathway for building a more structured foundation in this aspect of the work.
How BU’s MS in Software Engineering for AI Curriculum Connects to MLOps Engineering
At its core, the Master’s in Software Engineering for AI curriculum is intended to prepare students to build, deploy, and scale production-grade AI systems in real-world environments. As an outcome of this online degree program, students learn how to not only understand AI models but also deploy and scale them in a reliable, secure, and ethical manner.
Coursework in Scalable Systems and Software Engineering at Scale
Our MS in Software Engineering for AI program includes relevant coursework in scalable systems and software engineering at scale, such as:
- Data Algorithms for Scalable Systems
- Software Engineering at Scale
This coursework helps students better grasp the architecture and performance demands of AI systems, preparing them for future roles in MLOps engineering.
Data Design and Distribution at Scale, AI/ML Ops
In the second year of the program, students take a Data Design and Distribution at Scale, AI/ML Ops class. This course helps build career-focused skills in pipeline design, deployment thinking, and operational AI workflows.
AI/LLM-Aided Software Development and Responsible AI
MLOps engineers are frequently tasked with evaluating generated code, supporting maintainability, and ensuring that models are operating responsibly. In BU’s program, students learn how to assess AI-generated code not just for correctness but for security, maintainability, and ethics as well. Within the second year, learners even complete coursework on Responsible and Ethical Data Science and AI for this purpose.
A Year-Long Capstone That Brings It Together
Finally, our master’s degree in software engineering for AI culminates in a year-long capstone — where students design and deploy an end-to-end AI-enabled application at scale. All while connecting coursework to real-world production work, this project helps instill practical experience in:
- Modern release workflows
- Code review
- Data engineering
- Responsible AI implementation
Take the Next Step Toward Becoming an MLOps Engineer at BU
If you already hold an undergraduate software engineering degree (or related bachelor’s degree) and are ready to advance your education, the online MS in Software Engineering for Artificial Intelligence through Boston University is specifically designed for working professionals. With a 100% online learning format built with careers in mind, you’ll engage in weekly live sessions while establishing practical experience through collaborative and hands-on projects.
Request more information about this program, review the curriculum for yourself, or get started with your application for admission today. Additionally, you can attend a live webinar to further explore this program and get any lingering questions answered by our knowledgeable faculty and program representatives.