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About This Role
The AI Engineer III serves as the primary institutional owner and operational steward of the NebulaONE generative AI platform (TulaneAI) at Tulane University. This role is responsible for the administration, oversight, project design, and governance support of the university’s centrally hosted GenAI environment, built on Microsoft’s Azure AI Foundry and managed in partnership with CloudForce.
This position is a critical member of Tulane’s GenAI governance and technology team, supporting platform operations, vendor management, AI agent development oversight, and coordination across the GenAI Governance Committee and its subcommittees. The Administrator ensures the platform operates within institutional policy guardrails, enables effective adoption by faculty, staff, and researchers, and coordinates with IT, academic, research, compliance, and administrative stakeholders to advance responsible and scalable GenAI use across the university.
Required Education and Experience:
- Bachelor’s degree in information technology, computer science, information systems, or a related field
AND
- Minimum 3 years of experience in enterprise technology administration, cloud platform management, or IT project management
OR
Minimum 2 years of experience in AI/ML platform administration, SaaS product ownership, or GenAI tool deployment within a higher education or research environment
*
Preferred Qualifications:
- Master’s degree in information technology, data science, higher education administration, or a related field
- Experience with NebulaONE, WaveGPT, or comparable no\-code GenAI delivery platforms in a higher education setting
- Knowledge of AI guardrail frameworks, responsible AI practices, and agentic AI system design
- Familiarity with API integration, RAG (Retrieval\-Augmented Generation) agents, and low\-code/no\-code AI development
- Experience supporting institutional governance bodies, academic technology committees, or shared\-services technology models
- Certification in cloud administration (e.g., Microsoft Azure Administrator) or project management (PMP)
- Understanding of token\-based AI consumption models, cost management, and licensing structures
- Demonstrated ability to administer cloud\-hosted SaaS platforms, including user management, configuration, and feature enablement
- Familiarity with Microsoft Azure, Azure AI Foundry, and enterprise identity management (Entra ID / Azure Active Directory)
- Strong project management skills with the ability to coordinate multi\-stakeholder technology initiatives
- Knowledge of data governance, information security, and compliance frameworks including FERPA, HIPAA, and institutional data classification policies
- Ability to serve as a product owner, translating institutional needs into platform configurations, AI agent designs, and operational workflows
- Experience managing vendor relationships, software contracts, and service\-level agreements
- Strong written and verbal communication skills for presenting to technical and non\-technical audiences, including executive leadership
- Capacity to support and coordinate governance committee operations, including documentation, reporting, and policy tracking
Ability to manage platform adoption initiatives including training, onboarding, and user support coordination
*
Special Required Ability for Incumbents Who Have Contact or Exposure to Animals or Animal Tissues:
Ability to complete and pass successfully the required occupational health screening referenced in the University’s Animal Handler Health Surveillance Program on an annual basis.
Required Background Check, Physical, and Drug Screening for Incumbents Who Have Contact or Exposure to Animals or Animal Tissues:
Selected candidates must complete and pass a background check and an occupational health screening as a condition of employment. For identified jobs, a drug screening will also be required. The background investigation, required occupational health screening, and any required drug screening will be conducted after a conditional employment offer has been extended.
NCAA Related:
Ensures that all Department of Athletics and University\-related activities operate in full compliance with university, conference, and NCAA rules and regulations. Attends and participates in scheduled compliance seminars and complies with all the Athletic Department’s efforts to monitor compliance with NCAA regulations. Seeks interpretations from the compliance staff before acting when necessary. Reports potential rules violations immediately. Completes required compliance paperwork in a timely and efficient manner. Monitors all of their areas and/or program activities closely to maintain compliance with applicable rules and regulations.
Tulane University is an equal opportunity educator and employer committed to providing an education and employment environment free of unlawful discrimination, harassment, and retaliation. Legally protected demographic classifications (such as a person’s race, color, religion, age, sex, national origin, shared ancestry, disability, genetics, veteran status, or any other characteristic protected by federal, state, or local laws) are not relied upon as an eligibility, selection or participation criteria for Tulane’s employment or educational programs or activities.
Tulane University is responsible for providing reasonable accommodations to individuals with disabilities throughout the applicant screening process. If you need assistance in completing an application or during any phase of the interview process, please contact the Office of Human Resources by phone at 504\-865\-4748 or by email at [email protected] .
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 Tulane 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.
Tulane University AI Hiring
Tulane University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New Orleans, LA, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 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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