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Description
Indiana Tech is a thriving, independent, teaching-oriented university that delivers career-oriented programming in a wide variety of disciplines that includes business, engineering, computer sciences, criminal justice, cybersecurity, marketing, health sciences, communication and helping professions. Indiana Tech enrolls nearly 1,500 students in its traditional undergraduate programs on its picturesque main campus, and nearly 5,000 students in its adult and online program. We offer degrees at the associate, bachelor’s, master's, and doctoral levels and undergraduate and graduate certificates.
Over 41% of our students are either globally or domestically diverse; this contributes to our unique vibrant and inclusive campus community. All academic programs are designed to provide students with relevant and practical experiences, including an emphasis on internships. The university is accredited by the Higher Learning Commission.
Indiana Tech is committed to fostering a welcoming and respectful environment for all. We actively support the recruitment, engagement, and retention of students, faculty, and staff from a wide range of experiences, backgrounds, and perspectives. We believe that creating a sense of connection and support across our campus community plays an essential role in preparing students to succeed and contribute in a global society.
Indiana Tech has big plans for its future. Learn more at Indiana Tech Strategic Plan or strategicplan.indianatech.edu .
Indiana Tech is seeking an innovative, collaborative, and faculty-centered leader to serve as its AI Faculty Strategist. Reporting to the Executive Director of the AI Institute, with a dotted-line relationship to the Vice President for Academic Affairs, this position serves as the university’s primary faculty consultant for artificial intelligence pedagogy and instructional innovation. The AI Faculty Strategist leads Indiana Tech’s faculty-facing initiative for teaching AI within the curriculum and helps faculty build the knowledge, confidence, and instructional judgment needed to effectively integrate AI into teaching and learning.
Serving concurrently as Director of the Teaching Excellence Center, the AI Faculty Strategist plays a central role in advancing Indiana Tech’s institutional AI strategy by helping faculty navigate the rapidly evolving landscape of artificial intelligence in higher education. This position works closely with the Warrior AI Readiness Manager, the Director of Curriculum Design & AI Integration, academic deans, instructional designers, and faculty across disciplines to move instructors from AI awareness to meaningful course and program transformation.
The AI Faculty Strategist serves as the primary point of contact for faculty seeking guidance on AI pedagogy, instructional design, assessment redesign, and responsible AI integration. The successful candidate will provide individual consultation, facilitate professional development opportunities, mentor faculty participating in AI innovation initiatives, and help faculty identify opportunities for course redesign and instructional improvement. Faculty projects selected for full redesign and implementation will be carried out in partnership with the Curriculum Design team.
Additional responsibilities include developing and facilitating workshops, seminars, communities of practice, and faculty-development programs focused on AI in higher education. The Strategist will use institutional readiness data to identify coaching opportunities, contribute faculty-focused programming to university-wide AI initiatives, create instructional resources and teaching guides, support grant-related activities, and represent Indiana Tech’s AI efforts at institutional, regional, and national conferences.
Required Skills and ExperienceThe ideal candidate will possess a master’s degree in education, educational technology, instructional design, higher education, computer science, artificial intelligence, business, or a related discipline, along with a minimum of five years of higher-education teaching experience.
Candidates must demonstrate a strong understanding of artificial intelligence applications in higher education, including their impact on teaching, learning, assessment, curriculum design, and student engagement. The successful candidate will have a record of instructional innovation and experience integrating emerging educational technologies into academic programs and classroom environments.
The position requires demonstrated excellence in college teaching, outstanding written and verbal communication skills, and the ability to engage faculty from a wide variety of academic disciplines. Candidates must possess strong consultation, facilitation, coaching, and presentation skills and be able to foster collaboration while supporting faculty through instructional change and innovation.
Experience in designing and facilitating faculty development programs, workshops, training sessions, or professional learning initiatives is required. Candidates should also demonstrate the ability to translate complex AI concepts into practical instructional strategies that can be implemented effectively in diverse academic settings.
Preferred Skills and ExperienceA doctoral degree in education, instructional design, higher education, educational technology, artificial intelligence, computer science, business, or a related discipline is preferred.
Preferred candidates will have scholarship related to teaching and learning, educational technology, instructional innovation, or artificial intelligence. Experience supporting online education, digital learning environments, and faculty adoption of emerging technologies is also highly desirable.
Additional preferred qualifications include experience leading institutional teaching and learning initiatives, developing instructional resources and toolkits, presenting at professional conferences, managing faculty innovation programs, or supporting grant-funded educational projects.
Opportunity and ImpactThis is an exciting opportunity for a passionate educator and thought leader to help shape the future of teaching and learning at Indiana Tech. The AI Faculty Strategist will play a critical role in helping faculty understand, evaluate, and responsibly integrate artificial intelligence into their courses while strengthening student learning outcomes and preparing graduates for an AI-enabled workforce.
Success in this role will be reflected through increased faculty participation in AI professional development, growth in faculty consultations and curriculum redesign activity, development of instructional resources and teaching guides, improvement in AI-informed teaching practices, scholarly contributions, conference presentations, and achievement of grant-supported initiatives.
Why Indiana Tech?Indiana Tech is a private, nonprofit university dedicated to innovation, collaboration, and student success. As part of the AI Institute and Teaching Excellence Center, the AI Faculty Strategist will have the opportunity to influence teaching practices across the university and help position Indiana Tech as a leader in responsible and effective AI integration in higher education.
Ready to help faculty transform teaching and learning for the age of artificial intelligence? Apply today and join Indiana Tech in shaping the future of higher education through innovation, collaboration, and instructional excellence.
**Application materials must include Vita and unofficial transcripts. Applicants must be prepared to provide a written response to Tech's mission and vision. The position remains open until filled; a review of candidates will commence as application materials are received.
Qualifications
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Indiana Tech, 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Indiana Tech AI Hiring
Indiana Tech has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Fort Wayne, IN, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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