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About This Role
Req Id: 42152
Job Title: Cloud/AI Engineer
City: REMOTE
Job Summary
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Cloud \& AI Infrastructure Engineer
Location: REMOTE
The Cloud/AI Engineer will champion Purdue’s Cloud and AI initiatives as a fully competent and productive professional contributor. This position will work as a member of the IT Infrastructure team to develop, implement, optimize, and maintain access to cloud\-based AI technologies for IT Teams, researchers, and faculty. Responsibilities include deploying and debugging cloud stacks, developing and implementing policies for the use of AI via cloud infrastructure and software services, managing requests for new technology, establishing a secure cloud environment, ensuring appropriate availability of services, educating teams on new cloud and AI initiatives, ensuring the security of the cloud infrastructure, and creating and maintaining automation procedures. Will contribute to the development of operating procedures which support the AI and cloud infrastructure environment and effective use of resources. Identify the most optimal cloud and AI\-based solutions for the given need and maintain cloud infrastructures in accordance with best practices and security policies. Should have excellent troubleshooting skills and stay current with industry trends. Will participate in on\-call rotation with infrastructure teams.
*This is a remote position.*
About Us
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*When you join Purdue University, you join a community that keeps moving forward. For more than 150 years, we’ve been known for not only our groundbreaking work in STEM research, but also for our collective imagination, ingenuity, and innovation.*
What We're Looking For
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*Education and Experience Required:*
- Bachelor's degree in computer science, information technology, or a similar field
At least Three (3\) years of experience in the field of cloud computing
*
*Skills Required:*
- Azure, AWS, and GCP certifications preferred. Experience with tools such as Terraform, CloudFormation
- Ability to:
+ interact with a variety of customers in a professional manner
+ work independently and as a part of a team
- Strong collaboration and communication skills
- Proficient in networking
Troubleshooting and analytical skills
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*Preferred:*
- Experience with:
+ SysOps
+ containerization environments
+ configuration automation tools including Ansible, Chef, and/or Puppet
+ creating CI/CD pipelines for integrating IaC deployments into a deployment workflow supporting dev/test as well as production environments
- Experience:
+ developing solutions around well\-known AI models such as ChatGPT, PaLM, and Llama
+ using version control systems (like Git) to manage IaC configurations
- Azure, AWS, and GCP certifications preferred. Experience with tools such as Terraform, CloudFormation, Azure ARM/Bicep, and/or GCP Deployment Manager
Hypervisor/Virtualization experience
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*Additional Information:*
- Purdue University will not sponsor employment authorization for this position
- A background check will be required for employment in this position
- FLSA: Exempt (Not Eligible for Overtime)
- Retirement Eligibility: Defined Contribution Waiting Period
Benefit Statement: Purdue University offers a substantial Benefit Package including medical, dental, and vision insurance as well as a generous paid time off package for sick and vacation days
*
Career Stream
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Professional 2
- Pay Band S080
- Job Code \#20004159
Career path maker: https://www.purdue.edu/hr/careerpathmaker/
Who We Are
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*Purdue is a community built on collaboration, with global perspectives, Boilermaker pride and endless opportunity to live, learn and grow. Join us and contribute to our culture.*
Equal Opportunity Employer
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Purdue University is an EOE employer.
Posting Start Date: 5/29/26
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Purdue 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Purdue University AI Hiring
Purdue University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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