Senior AI Cloud Engineer

Job Profile:

Applications Developer 4

Job Family:

IT Applications Time Type:

Full time Apply before 11:59 PM Arizona time the day before the posted End Date.

Minimum Qualifications:

Bachelor's degree and seven (7) years of experience appropriate to the area of assignment/field; OR, Any equivalent combination of experience and/or training from which comparable knowledge, skills and abilities have been achieved.

Job Description:

Arizona State University is seeking a Senior Software Engineer, MCP / Full-Stack AI Platform Engineer to join the AI Acceleration Team within Enterprise Technology. This role will help design, build, and scale the technical infrastructure that enables ASU’s next generation of AI-powered learning, research, and operational tools. The engineer will focus on the Model Context Protocol (MCP), which connects AI applications to external tools, data sources, workflows, and enterprise systems in a secure, governed way.

The successful candidate will serve as a senior technical contributor and architecture partner for ASU’s AI platform ecosystem, including CreateAI , ASU’s secure, model-agnostic AI platform. This person will build production-grade MCP servers, full-stack AI-enabled applications, APIs, AWS infrastructure, data integrations, and reusable patterns that help ASU safely connect AI systems to university services. This is a hands-on engineering role for someone who is comfortable moving between architecture, backend development, frontend development, cloud infrastructure, security design, and stakeholder collaboration .

The ideal candidate is technically deep, product-minded, mission-driven, and excited to help ASU build tools for the future of learning. The AI Acceleration Team is responsible for helping ASU build, govern, and scale responsible AI capabilities across the institution. This role will help the team in building secure, agentic, tool-connected AI systems that can support meaningful university workflows.

The team supports ASU’s AI strategy by:

Building and maintaining secure AI infrastructure and platforms, including CreateAIDeveloping AI-powered tools that support learners, faculty, researchers, and staff

Creating reusable patterns for responsible AI development

Supporting enterprise integrations between AI systems and university data/services

Partnering across ASU to build culture, literacy, trust, and responsible adoption around AIAdvancing ASU’s vision for the future of learning through personalized, ethical, and accessible AI experiences

Salary Range: $140,000- $168,100/ Depends on Experience Essential Duties:

MCP Platform Engineering Design, build, deploy, and maintain production-grade MCP servers and clients.

Implement MCP capabilities that allow AI applications to securely access approved tools, resources, prompts, and enterprise data.

Develop reusable MCP templates, SDK wrappers, reference services, and starter patterns for ASU engineering teams.

Integrate MCP-enabled services with CreateAI and other ASU AI applications.

Track the evolving MCP specification and recommend updates to ASU implementation patterns.

Evaluate emerging agentic interoperability standards beyond MCP and advise on practical adoption.

Full-Stack AI Application Development Build full-stack applications and platform features that support AI-enabled workflows.

Develop backend services, REST APIs, serverless functions, frontend interfaces, and integration layers.

Work with modern JavaScript/Type Script frameworks such as React or Next.js.

Develop backend services in Python, Node.js, or comparable languages.

Implement secure user experiences that make complex AI capabilities accessible to non-technical users.

Rapidly prototype AI-driven experiences, validate usability, and mature successful prototypes into reliable production systems.

AWS Cloud Architecture and Infrastructure Design and implement AWS-native services using technologies such as:

LambdaAPI Gateway S3

DynamoDBCloud

FrontSQS/SNSEvent Bridge Cloud Watch Secrets  Manager Open Search Bedrock Build  infrastructure-as-code using Terraform or comparable tools.

Design scalable, resilient, cost-conscious cloud architectures.

Implement deployment pipelines,…