Artificial intelligence is no longer about isolated algorithms—it is about architecting resilient, autonomous systems that power the next economy. The Master of Science in Artificial Intelligence Engineering equips technical specialists to design, scale, and govern production-grade intelligent software. Moving decisively beyond introductory data science, the curriculum immerses engineers in advanced neural architectures, agentic workflows, foundation model orchestration, and distributed cloud inference. Graduates emerge prepared to direct enterprise AI deployments, eliminate architectural bottlenecks, and lead elite engineering teams at the technological frontier.
Duration
2 Years
Flexible Schedule
Format ⓘ
Flexible
On-Campus
P-Hybrid
Credits
36
Total credit hours
Transfer Credit
Up to 9
From accredited institutions
Average Class Size
18
Students per class
Building intelligent systems demands more than prompting models—it requires deep systems engineering. Positioned in Washington, D.C., steps from federal innovation hubs, defense contractors, and global technology corridors, Bay Atlantic University connects graduate engineers directly to the environments where enterprise AI is designed and deployed. Here, the capital’s tech ecosystem isn’t context—it’s coursework.
Modern engineering requires systems that reason, adapt, and execute autonomously. The curriculum moves students immediately past routine predictive modeling into architecting production-grade multi-agent coordination frameworks and sub-agent workflows. Students develop command over foundational model orchestration, high-performance vector retrieval pipelines, cloud-native inference engines, and autonomous problem-solving architectures. Guided by scholar-practitioners who build and deploy enterprise AI systems, you develop the architectural discipline required to turn raw algorithmic logic into scalable, autonomous infrastructure.
True engineering authority is forged under production constraints, not simulated exercises. Inside the CyberEdge Innovation Hub, graduate students pressure-test models against live latency, throughput, and compute bottlenecks. Students collaborate on high-stakes builds—competing in national and regional challenges including the Agentic AI Hackathon at DevFest DC and Cisco/WWT Discovery Week, where BAU teams consistently earn top podium finishes. Instruction is anchored in active industry toolchains, ensuring candidates bridge cutting-edge research with commercial enterprise delivery.
Technical education should accelerate an engineering trajectory, not delay it. Through intensive graduate capstone engagements, candidates take full architectural ownership of end-to-end AI pipelines—integrating enterprise cloud orchestration, fine-tuning domain-specific models, and solving genuine institutional bottlenecks. Backed by NECHE accreditation and an international network representing more than 40 countries, BAU graduates enter the marketplace prepared to command senior roles as Principal AI Engineers, Machine Learning Solutions Architects, and Chief Technology Officers (CTOs).
At BAU, applied R&D and advanced industry practicums play a crucial role in linking artificial intelligence theory with enterprise practice. These experiences allow graduate students to collaborate with tech leaders to orchestrate autonomous systems, design complex neural networks, and solve high-level machine learning challenges. With the introduction of the Cyber Edge Innovation Hub, students have the opportunity to utilize powerful computing resources, work with AI experts through professional mentorship, and leverage experiential learning, including competitive AI hackathons and campus initiatives, to confidently launch their own intelligent tech ventures.
$175,140
2025 Median Pay*, Computer and Information Systems Manager
$140,300
2025 Median Pay*, Computer and Information Research Scientist
Other Career Opportunities: Principal Machine Learning Specialist • Chief Technology Officer (CTO) • Lead AI Engineer • Multi-Agent Systems Architect • Director of AI Engineering • Generative AI Specialist • NLP (Natural Language Processing) Engineer
*Source: U.S. Bureau of Labor Statistics.
Advanced curriculum designed for real-world artificial intelligence challenges
36+
Credit Hours
40+
Courses
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Tuition Assistance
Up to $30,000
DMV Tuition Assistance
Up to 60%
Payment Plans
0% interest
Rolling admissions with multiple intake periods throughout the year. Domestic and transfer applications may continue to be reviewed after these deadlines on a rolling basis, subject to program availability. New students must complete registration by the census date for the applicable term; otherwise, they will not be eligible to register.
In this highly connected world, technology leaders must think innovatively and act responsibly. Our faculty incorporates critical topics such as intelligent systems design, secure network architectures, and global data strategies, preparing students to make technical decisions aiming not only toward optimal system performance but also to make a positive, lasting impact on the digital environment and society.
Chair of the Information Sciences and Engineering
EDUCATION
Doctor of Science in Cybersecurity
RESEARCH FOCUS
Non-Destructive Method of Detecting Hardware Attack, PII Security Controls, Web Server Protection, and Bilingual Translator Programming
HIGHLIGHTS
Chair of the Information Sciences and Engineering
EDUCATION
Doctor of Science in Cybersecurity
RESEARCH FOCUS
Non-Destructive Method of Detecting Hardware Attack, PII Security Controls, Web Server Protection, and Bilingual Translator Programming
HIGHLIGHTS
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