Online Master of Science in Software Engineering for Artificial Intelligence

This program prepares engineers who can do something rare in industry today: combine strong software engineering best practices, the ability to architect systems that operate with data at scale, and a foundational understanding of AI. Most engineers have one or two of these skills — this degree is built to develop all three. The Online MS in Computer Science with Artificial Intelligence goes deeper into how machine learning models are built and how they work internally, while this program focuses on using those models to build large-scale software systems, giving you enough foundational understanding of how models work to get data in and out of them, then shifting the focus to scalable systems and software engineering practices. The MS in Enterprise AI program, offered by BU’s Faculty of Computing & Data Sciences, is the technical counterpart to the AI in Business program from Questrom — it focuses on operationalizing AI deployments inside organizations rather than building production-grade AI software systems.

Boston University’s online Master of Science in Software Engineering for Artificial Intelligence is designed for technical professionals who want to build, deploy, and scale AI-enabled systems in real-world environments. The program is ideal for experienced software engineers expanding beyond traditional application development into machine learning and LLM integration; aspiring engineers with strong technical foundations preparing for AI-focused roles in scalable system design, MLOps, data engineering, and responsible AI deployment; engineers transitioning into AI-enabled roles from backend, platform, or systems engineering; and technical professionals seeking to future-proof their careers as AI becomes embedded in nearly every industry. The program is particularly well suited to working professionals, and we expect most students to take part in the program alongside their work.

Software engineering for AI is emerging because traditional software engineering and data science alone aren’t sufficient to build production-grade AI systems. AI systems behave probabilistically, require continuous monitoring, involve complex data dependencies, and need rigorous engineering practices to be reliable, scalable, and secure.

Graduates of the master’s degree in artificial intelligence and software engineering are prepared for technical roles that focus on building and operating AI-enabled systems in production environments. These roles include AI Software Engineer — designing applications that integrate machine learning models and LLMs into scalable software systems; MLOps Engineer — building and managing the infrastructure that supports AI deployment, including data pipelines, monitoring, and cloud environments; Platform Engineer — developing the underlying platforms and tools that allow teams to build, deploy, and scale AI-enabled applications efficiently; and Software Architect for Intelligent Systems — designing the overall architecture of AI-enabled software systems for scalability, security, and ethical implementation. Because the software engineering courses and AI curriculum emphasize production-grade systems, cloud-based deployment, and responsible implementation, graduates are prepared not just to build AI models, but also to lead the engineering efforts required to make AI function reliably at scale.

The Master of Science in Software Engineering for Artificial Intelligence includes both foundational preparation and advanced AI-focused coursework. AI and software engineering courses include Machine Learning Fundamentals; Programming Toolkit for Data Science; AI/LLM-Aided Software Development; Responsible and Ethical Data Science and AI; Data Design and Distribution at Scale, AI/ML Ops; Human-Centric AI UX; Data Algorithms for Scalable Systems; Software Engineering at Scale; and the Capstone Project: End-to-End AI Application Utilizing Big Data at Scale. Throughout the AI program, students work with industry-relevant tools and production-style workflows, including cloud-based deployment environments, modern release workflows, AI-assisted development and code review tools, data engineering frameworks, and scalable data pipelines and MLOps workflows.

The two primary languages are Python and Java. Python is used in the machine learning and data science modules, and Java is used in the general software engineering modules. Students will also work extensively with one major cloud provider (AWS, Google Cloud, or Azure), large-scale data technologies such as Kafka or Google Pub/Sub, and database systems including Postgres and non-relational alternatives suited to scale. GitHub will be used heavily across every module.

Boston University’s online software engineering degree prepares you to design, build, and deploy AI-enabled software systems that are secure, scalable, and production-ready. Throughout the program, you’ll develop skills in integrating AI and large language models into large-scale software systems; applying disciplined software engineering practices to AI-enabled applications; designing scalable data pipelines and MLOps workflows; evaluating AI-generated code for correctness, security, and maintainability; cloud-based deployment and modern release workflows; data engineering and distributed systems design; responsible and ethical AI implementation; and human-centered system design and AI UX. Through hands-on assignments and a year-long capstone project, you’ll build applied experience with industry-relevant tools — graduating with a portfolio that demonstrates your ability to operate at the intersection of software engineering, AI, and systems architecture.

In the online software engineering degree for AI, you’ll learn to solve the challenges organizations face when moving AI from experimentation into production, including integrating AI and large language models into existing software systems without compromising reliability or performance; designing scalable data pipelines and MLOps workflows that support model training, deployment, and monitoring; evaluating AI-generated code for correctness, security, and maintainability; deploying AI-enabled applications in cloud environments using modern release workflows; building systems that are trustworthy, explainable, and human-centered; and ensuring responsible and ethical AI implementation within real-world constraints. Through hands-on assignments and a year-long capstone project, you’ll apply these AI skills to develop an end-to-end AI-enabled application at scale.

Deep learning is covered as part of the Machine Learning Fundamentals module, typically across one to two weeks of that course.

The course is designed to be tool-agnostic so that it stays current as the landscape evolves. Today, that may mean working with a leading model such as Claude, but the focus is on durable principles rather than any one tool — best practices for validating and verifying AI-generated code, building automated test suites that give confidence in generated code, and designing multi-agent systems that handle code generation, validation, and deployment.

The capstone is a year-long, hands-on project where students design and deploy an end-to-end AI-enabled application at scale, applying everything learned across the program — software engineering fundamentals, AI integration, data architecture, MLOps workflows, and responsible AI practices. Each student completes their own individual capstone, but collaboration with peers is strongly encouraged throughout. The program provides guardrails and project ideas, but within those guardrails students have significant freedom to choose a topic. Wherever possible, students are encouraged to design a capstone that is directly relevant to their professional context — something they can use in their current role or showcase in future job applications. By the end, students graduate with a portfolio-ready project that demonstrates their ability to integrate AI reliably, securely, and responsibly into large-scale software systems.

Assessment is primarily hands-on. Students complete project-based work in each module, culminating in the year-long capstone. Larger summative assessments combine a built artifact with a written reflection on how the student arrived at the solution. Smaller formative assessments are embedded in the asynchronous content to check understanding as students progress.

At the standard pace, the program takes four semesters (roughly 16 months) with students typically spending 20 to 25 hours per week. Students taking a slower pace can expect about 10 to 15 hours per week and complete the program over six to eight semesters. The first cohort begins in fall 2026, and students starting then on the standard pace would graduate in December 2027.

The AI and software engineering program is designed for working professionals and follows a structured, high-engagement weekly format. Each week typically includes on-demand asynchronous coursework with recorded lectures and structured learning modules, hands-on assignments and applied projects focused on real-world workflows, weekly live sessions taught from BU Virtual Studios where students engage directly with faculty, collaborative project work and peer interaction, and personalized feedback from instructors and learning facilitators. The program blends flexibility with structure, allowing students to learn on their schedule while still benefiting from live faculty interaction.

The bootcamp is designed to take about two weeks, or roughly 20 total hours of work (about 10 hours per week) completed before the first semester begins. It covers foundational skills the program assumes from day one, including GitHub fluency, basics of Python, and familiarity with code review processes. Students who already have these skills will likely move through it much faster. A separate orientation helps students get comfortable navigating the fully online learning environment.

Online students in the Master’s in Software Engineering for Artificial Intelligence benefit from the same academic standards and institutional support available across Boston University. Support includes dedicated learning facilitators who provide guidance, feedback, and structured support throughout each course; direct access to BU Engineering faculty through weekly live sessions and ongoing interaction; academic advising and student success support to help students navigate coursework and stay on track; and access to BU’s global alumni network and benefits, offering long-term professional connections and networking opportunities. The program is intentionally designed to combine flexibility with strong engagement, ensuring online students are supported academically, professionally, and as members of the broader Boston University community.

Yes. The program includes fireside chats with industry professionals throughout, and the College of Engineering is actively bringing in industry voices wherever possible. Some capstone project ideas may also be shaped by real needs from industry partners. It does not offer industry placements or internships.

We ask all interested applicants to complete our online application at bu-eng.cas.myliaison.com.

To apply to the AI and software engineering program, you must hold a bachelor’s degree with relevant coursework in areas such as computer science, software engineering, data science, engineering, or related technical fields. Professional experience is valued, and applicants from a range of technical backgrounds are encouraged to apply. The program does not require standardized test scores (such as the GRE or GMAT), and professional references are optional. The software engineering and AI master’s program offers start terms in September and January, and applicants should review the BU Engineering admissions page for specific application deadlines and submission requirements.

No. The program is designed as a structured, linear sequence of courses, and transfer credits from other institutions are not accepted.

The software engineering and artificial intelligence degree prepares you to advance into AI-enabled engineering roles by building the skills needed to design, deploy, and scale production-grade AI systems. As AI becomes embedded in modern software infrastructure, organizations need engineers who understand not just models, but how to integrate them reliably, securely, and ethically into real-world systems. This artificial intelligence master’s program equips you with applied experience in scalable system design, AI integration, MLOps workflows, cloud deployment, and AI-assisted development — all aligned with industry-relevant tools and production-style practices. Graduates leave with a portfolio of applied work that demonstrates their ability to operate at the intersection of software engineering, AI, and systems architecture.