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About This Role
Job Profile:
Applications Developer 2
Job Family:
IT Applications
Time Type:
Full time
Max Pay – Depends on experience:
$120,000\.00 USD Annual
Apply before 11:59 PM Arizona time the day before the posted End Date.
Minimum Qualifications:
Bachelor's degree and three (3\) 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 Profile Summary:
Assists in design, development, and troubleshooting software and applications in support of establishing a sustainable institutional technological infrastructure.
Job Description:
Are you experienced in both enterprise Salesforce development and modern AI engineering practices? Can you balance rapid experimentation with enterprise governance and scalability? Comfortable working in fast\-paced Agile and innovation\-focused environments? The keep reading below for this Salesforce AI Engineering opportunity!
ASU EdPlus is a dynamic unit of Arizona State University focused on the design and scalable delivery of digital teaching and learning models to increase student success and reduce barriers to achievement in higher education. We advance the economic, social, cultural, and overall health of the local, national, and international communities served by ASU.
Want to know more about working at EdPlus? Watch this short video about working at EdPlus!
NOTE: This is an in\-person, hybrid position. You must be able to reliably commute to Scottsdale, AZ.
We are seeking an innovative and highly technical Salesforce AI Engineer to join the EdPlus Product \& UX Technology team. Every day you will make a difference in the lives of others by leading the design, development, and deployment of AI\-powered student engagement and workflow automation solutions within the Salesforce ecosystem.
You will possess deep expertise in Salesforce platform development along with advanced experience in designing and deploying AI\-powered agents using Salesforce Agentforce. You must have strong capabilities in prompt engineering, Apex development, AI workflow orchestration, agentic architectures, and enterprise DevOps practices.
You will work closely with architects, business stakeholders, product owners, analysts, and coaching operations teams to build scalable AI\-assisted solutions that reduce manual work, automate administrative processes, and improve personalized student engagement at scale. You will bring a passion for improving student outcomes through intelligent technology solutions.
Essential Duties:
- Design, develop, deploy, and maintain AI\-powered solutions within the Salesforce ecosystem.
- Build and configure autonomous AI agents using Salesforce Agentforce.
- Develop and maintain Agent Actions that enable agents to perform complex business operations and orchestrate workflows.
- Create, optimize, and ground prompt templates for Large Language Model (LLM)\-based interactions.
- Design and implement AI\-assisted coaching workflows and intelligent automation solutions.
- Develop Apex classes, Lightning Web Components (LWC), Flows, and APIs that support AI\-driven business processes.
- Build sophisticated engagement\-based workflow automation and personalized outreach journeys.
- Integrate Agentforce solutions with Salesforce data models, external systems, APIs, and enterprise services.
- Configure and maintain Retrieval\-Augmented Generation (RAG) and grounding strategies for AI accuracy and contextual relevance.
- Collaborate with stakeholders to identify automation opportunities and translate business requirements into scalable AI solutions.
- Configure and support CI/CD pipelines and Git\-based deployment workflows.
- Work with MCP servers and AI\-assisted development tooling to improve engineering productivity and accelerate delivery.
- Monitor AI solution performance, prompt effectiveness, agent behavior, and operational reliability.
- Ensure AI solutions adhere to enterprise security, compliance, governance, and responsible AI standards.
- Participate in architecture reviews, sprint planning, release management, and Agile ceremonies.
- Create and maintain technical documentation related to AI workflows, prompt engineering, integrations, and deployment processes.
- Assume or coordinate other duties or projects as assigned or directed.
*AI \& Automation Responsibilities*
- Build AI\-powered agents capable of reasoning over student engagement and operational data.
- Design intelligent workflows that automate Tier 1 coaching and administrative tasks.
- Develop prompt templates that provide grounded, context\-aware AI responses.
- Create engagement\-based automation journeys that personalize outreach at scale.
- Implement scalable AI governance, monitoring, and optimization practices.
NOTE: This is an in\-person, hybrid position. You must be able to reliably commute to Scottsdale, AZ.
Desired Qualifications:
- Bachelor’s degree in Computer Science, Information Technology, Engineering, Artificial Intelligence, or related field.
- More than four years of Salesforce development experience and more than two years of experience developing AI\-enabled or intelligent automation solutions.
- Strong hands\-on experience with Salesforce Agentforce, Agent Actions, Prompt Builder / Prompt Templates, Apex, Lightning Web Components (LWC), Salesforce Flow, SOQL/SOSL, and Salesforce APIs.
- Experience designing and deploying autonomous or semi\-autonomous AI agents, AI copilots, conversational AI systems, or intelligent workflow orchestration platforms.
- Experience with prompt engineering, grounding techniques, Retrieval\-Augmented Generation (RAG), and integrating AI services or LLM\-powered workflows into enterprise applications.
- Experience working with Salesforce Sales Cloud, Service Cloud, Education Cloud, Einstein AI, AI Cloud, Prompt Builder, and Data Cloud.
- Strong experience with Git\-based version control systems such as GitHub or BitBucket, along with CI/CD pipeline configuration and Salesforce DevOps tools including Salesforce DX, GitHub Actions, Azure DevOps, Jenkins, Gearset, or Copado.
- Experience integrating external systems using REST/SOAP APIs, external integrations, vector databases, semantic search technologies, and asynchronous/event\-driven architectures.
- Experience using MCP\-based developer tooling and AI\-assisted coding platforms including Claude Code, Codex, GitHub Copilot, Salesforce MCP Servers, and related AI engineering tools.
- Strong understanding of Salesforce security architecture, governance, and enterprise\-scale implementations, along with excellent analytical, troubleshooting, communication, and collaboration skills; Salesforce AI Specialist and/or Platform Developer certifications preferred.
Desired Technical Skills:
- Salesforce Agentforce
- Agent Actions
- Prompt Engineering
- Prompt Builder
- Apex
- Lightning Web Components (LWC)
- Salesforce Flow Automation
- AI Workflow Orchestration
- Salesforce DX (SFDX)
- GitHub / BitBucket
- CI/CD Pipeline Configuration
- REST/SOAP APIs
- LLM Integration
- Retrieval\-Augmented Generation (RAG)
- AI Governance \& Responsible AI
- MCP Server Integrations
- JavaScript
- SQL/SOQL
- DevOps Automation
- Agile/Scrum
NOTE: Please answer the following questions in your cover letter:
- Describe your experience building complex logic in Salesforce Flow or Apex specifically to trigger external actions or AI responses. How do you handle “grounding” or ensuring data accuracy within a prompt?
- We are moving toward an 'Agentic' model using Agentforce. Explain how you would architect an Agent Action in Salesforce to solve a specific manual administrative task.
Salary \& Benefits:
$100,000 – $120,000 per year; DOE
ASU offers a total compensation package that includes valuable employee benefits. See the ASU Benefits website to explore options.
- Healthcare
- Financial Security
- Retirement
- Family Resources
- Tuition Reduction (Eligible ASU Employee, their dependents and spouse.)
- Discounts
Working Environment:
Your desk will be in the beautiful, cutting\-edge, and collaborative workspace at SkySong, the ASU Scottsdale Innovation Center. The Center houses a diverse business community that links technology, research, education, and entrepreneurship to position ASU and Greater Phoenix as global leaders in the knowledge economy.
ASU EdPlus supports flexible work options, ranging from alternate to hybrid work schedules, subject to approvals per ASU policy. (This is an in\-person, hybrid position.)
Applicant must be eligible to work in the United States. ASU EdPlus will not be a sponsor for this position.
Department Statement:
As a central enterprise unit for ASU, ASU EdPlus supports the university charter by focusing on the design and scalable delivery of digital teaching and learning models to increase student success and reduce barriers to achievement in higher education.
ASU EdPlus defines itself through a culture of curiosity, risk\-taking, and refusing to accept the status quo. Our employees are valued, respected, and encouraged to be their unique selves. We know that our ability to deliver high\-quality services and educational experiences is strengthened by our culture of innovation, driving outcomes through serving learners, achieving milestones, striving for excellence, solving problems, embracing urgency, and being bold.
ASU EdPlus
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$24414\.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 $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($110K) sits 50% below the category median. Disclosed range: $100K to $120K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
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: $120K - $120K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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