Senior DevOps Engineer, AI, Automation, & Data Engineering

Boston University • Boston, MA

15h • Hybrid

About The Position

Boston University is hiring a Senior DevOps Engineer for its AI and Data Engineering team. The role supports university initiatives to develop and operate AI applications, agents, and data solutions. It spans infrastructure, model management, and application development in cloud, on-premise, and hybrid environments.

The engineer will design and maintain scalable AI and data platforms; lead DevOps architecture, deployment strategies, automation, and infrastructure standards; and build and optimize containerized environments. Working with engineering teams, the role will support AI applications, agents, integrations, and data pipelines, along with emerging workflows such as RAG, MCP, and A2A. It will also involve hosting, configuring, and fine-tuning LLM and SLM models, establishing platform observability and reliability practices, and ensuring infrastructure meets security, compliance, governance, and operational standards. This is a hybrid role.

Requirements

• Degree in Computer Science, Software Engineering, or a related field.
• At least 5 years of applicable work experience.
• Strong understanding of DevOps practices, scalable architectures, and modern security standards.
• Expertise in containerization technologies, including Kubernetes, Docker, AWS EKS, and AWS ECS, and multi-environment deployment across on-premise, cloud, and hybrid environments.
• Development experience with modern programming languages and technologies; examples include Python, React, Vue, Node.js, TypeScript, APIs, databases, Git/GitHub, CI/CD, AI coding assistants, and Infrastructure as Code.
• Familiarity with AI technologies and frameworks, including prompt engineering, RAG, embeddings, MCP, and A2A.
• Hands-on experience hosting and fine-tuning LLMs or SLMs.

Responsibilities

• 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.

Benefits

• PTO and leave policy, paid intersession break, and 13 paid holidays.
• University-funded retirement plan, fully vested after 2 years of eligible service.
• Tuition assistance program for employees and family members.
• Staff learning opportunities, including lunch-and-learn sessions and an extensive online course library.
• Events and opportunities to engage with peers through the Fun Advisory Board, NERCOMP, and EDUCAUSE.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior