Interested in this AI/ML Engineer role at Arizona State University?
Apply Now →Skills & Technologies
About This Role
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 CreateAI
- Developing 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 AI
- Advancing 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/TypeScript 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:
+ Lambda
+ API Gateway
+ S3
+ DynamoDB
+ CloudFront
+ SQS/SNS
+ EventBridge
+ CloudWatch
+ Secrets Manager
+ OpenSearch
+ Bedrock
- Build infrastructure\-as\-code using Terraform or comparable tools.
- Design scalable, resilient, cost\-conscious cloud architectures.
- Implement deployment pipelines, CI/CD workflows, automated testing, and operational monitoring.
- Troubleshoot performance, reliability, security, and cost issues in production systems.
Security, Governance, and Responsible AI
- Partner with security and operations teams to define guardrails for MCP and AI integrations.
- Help assess risks such as:
+ Prompt injection
+ Tool poisoning
+ Data exfiltration
+ Over\-permissioned tools
+ Supply\-chain vulnerabilities
+ Insecure third\-party MCP servers
+ Sensitive data exposure
- Implement authentication and authorization using OAuth, OIDC, JWTs, scopes, service roles, and secrets management.
- Ensure AI integrations follow ASU expectations for privacy, FERPA\-aware design, responsible innovation, and human\-centered impact.
- Build logging, auditing, and usage analytics into MCP and AI services.
- Contribute to integration review processes and technical approval criteria.
Desired Qualifications:
- Hands\-on experience designing, building, or operating MCP servers, MCP clients, AI tools, or agentic AI integrations.
- Strong understanding of the Model Context Protocol, including tools, resources, prompts, transports, and security considerations.
- Experience with retrieval\-augmented generation, embeddings, vector search, prompt engineering, evaluation, and agentic workflows.
- Familiarity with AI safety, responsible AI practices, model evaluation, and governance.
- Experience with Node.js and modern frontend frameworks such as React
- Experience designing RESTful APIs, event\-driven services, and microservice\-style architectures.
- Experience with AWS cloud services, especially serverless architectures (AWS Lambda, API Gateway, S3, DynamoDB, IAM, CloudWatch, CloudFront, SQS)
- Strong background in API security, authentication, authorization, and secrets management, including OAuth, OIDC, and JWT.
- Experience supporting security reviews, incident response, threat modeling, or integration approval processes.
- Ability to explain complex technical concepts clearly to technical and non\-technical audiences.
- Strong written communication, documentation, and presentation skills.
- Demonstrated ability to model empathy, compassion, and emotional intelligence.
- Experience in a values\-driven organization with a strong commitment to inclusion and belonging.
- Ability to cultivate a psychologically safe environment where all team members can thrive.
- Capacity to inspire and drive meaningful change in individual, institutional, and corporate behaviors to support a more sustainable environment.
- Commitment to leading by example through effective communication, active participation, and advocacy for the institution’s sustainability programs.
Special Instructions:
In your cover letter, please describe your experience using artificial intelligence (AI), including the tools or technologies you have worked with. We also encourage you to share your passion for AI and how you have applied it to improve your work, solve problems, increase efficiency, or support innovation.
Working Environment
- Activities are primarily performed in an environmentally controlled office or hybrid work setting.
- Work requires regular use of a computer, keyboard, mouse, video conferencing, and collaboration tools.
- Role may require extended periods of sitting and focused technical work.
- Regular communication with team members, stakeholders, and university partners is required.
- Responsibilities may require changing priorities quickly, responding to production issues, and resolving ambiguity across teams.
Department Statement:
Enterprise Technology (ET) embraces its role as both an enabler and catalyst for advancing the vision and work of the New American University. We are a values\-driven organization. Our commitments are reflected in all of the work we do in pursuit of operational excellence, the experience and delight of our community, and our strategic and innovation initiatives. Applicants must be eligible to work in the United States.
Join the team that sparks human\-centered innovation
ET is a rapidly reconfigurable and entrepreneurial organization at ASU that prioritizes and executes to meet the needs of our community of learners, faculty, researchers and staff. Our work emphasizes autonomy, flexibility and distributed decision\-making to leverage the strengths of individuals. Together, we embrace a culture that nurtures, engages and embraces many voices with a shared lens of positive community impact and expanded opportunities for collaboration.
Why join us?
Mission oriented. Everything we do is to advance ASU’s charter \- measuring who we include and how they succeed. We are staunch champions of learner success and put people first.
Flexibility. Our flexible work environment allows employees to explore opportunities with their supervisor beyond the traditional work schedule. Opportunities include hybrid work where staff are in person three days per week.
Culture forward. We embrace a Positive Core culture: Belonging, Relational, Authentic, Visionary and Empowered.
Scale of impact. Our work changes the world. With 200k\+ learners, faculty, researchers and staff, working with ET means you have the capacity to improve many lives and entire communities.
World\-class, low cost education. Our professional development is built in! ET encourages staff to seek additional certificates and degrees via ASU’s top ranked programs with major tuition breaks.
Exposure to industry giants. ET partners with Amazon, Apple, Arista, Cox, Verizon, Salesforce, Alteryx and a diversity of technology companies to enhance our innovations and deepen their impact.
Driving Requirement:
Driving is not required for this position.
Location:
Off\-Campus: Scottsdale
Funding:
No Federal Funding
Instructions to Apply:
Current employees, student workers seeking staff opportunities, and students applying for student worker positions must apply directly through the Workday Jobs Hub.
Please use the link below to log in using single sign\-on.
https://www.myworkday.com/asu/d/inst/1$9925/9925$25702\.htmld
To be considered, your application must include all of the following attachments:
- Cover letter
- Resume or CV
Multiple documents may be uploaded in the attachments section. Alternatively, applicants may combine all required materials into a single PDF for submission. Please ensure uploaded documents are clearly labeled and include your name.
Please ensure your resume includes all employment information in month and year format, for example 6/04 to 8/14, along with job title, job duties, and employer name for each position. Your resume should clearly demonstrate how your experience and background meet the minimum and desired qualifications for this position. Incomplete applications or missing required materials may not be considered.
Important: Do not withdraw your application to make edits. Once an application is withdrawn, it cannot be edited, reactivated, or replaced with a new submission. If you have questions or need assistance, please contact The Office of Human Resources Talent Acquisition before the posting close date.
Graduate Assistant, Intern and part\-time positions are counted as half time for experience equivalency, meaning one year equals six months of experience.
Only electronic applications will be accepted for this position. By submitting an application, you confirm that the information provided is accurate and complete.
ASU Statement:
Arizona State University is a new model for American higher education, an unprecedented combination of academic excellence, entrepreneurial energy and broad access. This New American University is a single, unified institution comprising four differentiated campuses positively impacting the economic, social, cultural and environmental health of the communities it serves. Its research is inspired by real world application blurring the boundaries that traditionally separate academic disciplines. ASU serves more than 100,000 students in metropolitan Phoenix, Arizona, the nation's fifth largest city. ASU champions inclusive excellence, and welcomes students from all fifty states and more than one hundred nations across the globe.
ASU is a tobacco\-free university. For details visit https://wellness.asu.edu/explore\-wellness/body/alcohol\-and\-drugs/tobacco
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, or any other basis protected by law.
Notice of Availability of the ASU Annual Security and Fire Safety Report:
In compliance with federal law, ASU prepares an annual report on campus security and fire safety programs and resources. ASU’s Annual Security and Fire Safety Report is available online at https://www.asu.edu/police/PDFs/ASU\-Clery\-Report.pdf . You may request a hard copy of the report by contacting the ASU Police Department at 480\-965\-3456\.
Relocation Assistance – For information about schools, housing child resources, neighborhoods, hospitals, community events, and taxes, visit https://cfo.asu.edu/az\-resources .
Employment Verification Statement:
ASU conducts pre\-employment screening which may include verification of work history, academic credentials, licenses, and certifications.
Background Check Statement:
ASU conducts pre\-employment screening for all positions which includes a criminal background check, verification of work history, academic credentials, licenses, and certifications. Employment is contingent upon successful passing of the background check.
Fingerprint Check Statement:
This position is considered safety/security sensitive and will include a fingerprint check. Employment is contingent upon successful passing of the fingerprint check.
Salary Context
This $140K-$168K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Arizona State University, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($154K) sits 28% below the category median. Disclosed range: $140K to $168K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Arizona State University AI Hiring
Arizona State University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Scottsdale, AZ, US. Compensation range: $168K - $168K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.