Senior DevOps Engineer, AI, Automation, & Data Engineering

Overview

• Are you ready to challenge the status quo and help shape the future of higher education and beyond? At Boston University, we are assembling a world-class AI and Data Engineering team to transform operations, drive innovation, and deliver innovative AI applications and agents. This is your chance to work on cutting-edge technologies—Agentic AI frameworks, LLMs, RAG, MCP, scalable AI architectures—and develop solutions at the forefront of modern artificial intelligence. Join us as a Senior DevOps Engineer. As a member of the AI and Data Engineering team, you will tackle some of the university's highest priority initiatives while collaborating with our renowned faculty, brilliant students, and industry-leading technical teams. You'll bring your expertise to a vibrant environment that values ingenuity, agility, and impact, where your ideas can make a difference on a global scale. Together, we'll deliver AI solutions that not only revolutionize university operations but also become models of innovation for institutions worldwide. This hybrid role is perfect for individuals who thrive in dynamic environments, working across infrastructure, model management, and application development to bring impactful AI and Data solutions to life. Join us to make history, develop transformative technology, and work alongside some of the greatest minds in academia and technology. If you thrive in a fast-paced, high-performing environment and want to leave your mark on projects with real-world impact, we encourage you to apply for this exciting opportunity! Be part of the team that will define what's possible in AI for academia and beyond. You Will: Design and maintain scalable AI and Data platforms across cloud, on-premise, and hybrid environments. Lead DevOps architecture, deployment strategies, automation, and infrastructure standards. Build and optimize containerized environments using Kubernetes, Docker, AWS ECS, and EKS. Develop and support AI applications, agents, integrations, and data pipelines in partnership with engineering teams. Support emerging AI technologies and workflows, including RAG, MCP, and A2A. Host, configure, and fine-tune LLM and SLM models for university use cases. Establish monitoring, observability, performance, capacity, and reliability practices for AI and Data platforms. Ensure platforms and infrastructure meet security, compliance, governance, and operational standards. Identify and implement technical improvements that strengthen reliability, scalability, efficiency, and platform maturity.