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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 a visionary, collaborative, and results-driven leader to serve as its Executive Director of the AI Institute. Reporting to the Vice President for Academic Affairs, the Executive Director will provide strategic vision and operational leadership for Indiana Tech’s AI Institute. This position will build a coordinated pathway that moves faculty, staff, and students from AI awareness to hands-on curriculum redesign while positioning Indiana Tech as a regional leader in the responsible adoption of artificial intelligence in higher education.
The Executive Director will establish the Institute’s strategic vision, annual priorities, and multi-year roadmap in alignment with Indiana Tech’s Strategic Plan and the AIHE grant. This leader will oversee the Institute’s faculty AI-development pipeline, spanning awareness, coaching, and curriculum execution, and will supervise a multidisciplinary team responsible for AI readiness, faculty strategy, curriculum design and integration, regional partnerships, and intelligent institutional operations
The Executive Director will serve as principal administrator for the AIHE grant, with responsibility for budget oversight, milestone tracking, funder reporting, and renewal or expansion proposals. This position will also oversee the Institute’s operating and grant budgets and establish shared metrics and reporting tools that clearly communicate progress across the AI-development pipeline.
As the Institute’s principal liaison to the University AI Council, the Executive Director will ensure that programs, tools, and data practices align with Indiana Tech’s Artificial Intelligence Policy and applicable FERPA, GLBA, and data-governance obligations. This leader will convene and chair an AI Institute Advisory Board composed of academic deans, faculty, Information Technology, and Human Resources representatives to establish priorities and review new AI tools before campus-wide implementation.
The Executive Director will build regional employer and community partnerships, represent the AI Institute to grantors, accreditors, and external funders, and monitor the national AI-in-higher-education landscape to inform institutional priorities. Additional responsibilities include leading change-management and communication efforts, publishing an annual “State of AI at Indiana Tech” report, sponsoring a faculty AI fellows or seed-grant program, and supporting the hiring, onboarding, and performance management of Institute employees.
Required Skills and ExperienceThe successful candidate will possess a master’s degree in higher education administration, educational technology, computer science, artificial intelligence, business, or a related field. Candidates must have at least seven years of progressively responsible leadership experience in higher education, including experience supervising professional staff and managing budgets and personnel.
Candidates must demonstrate a strong understanding of generative AI technologies, their applications in higher education, and the related risks and compliance considerations, including FERPA, GLBA, and data governance. The position requires the ability to balance innovation with responsible technology adoption and to translate complex technical, academic, and compliance matters for varied institutional audiences.
Experience administering grant-funded programs, including budget oversight and funder reporting, is required. Candidates must also have experience developing partnerships with external organizations, such as employers, community organizations, or regional and national networks.
The successful candidate will possess outstanding written, verbal, and executive presentation skills and be prepared to represent Indiana Tech with external partners and interested parties. Candidates must demonstrate the ability to build consensus and advance complex initiatives across academic and administrative units with competing priorities.
Preferred Skills and ExperienceA doctoral degree in higher education administration, educational technology, computer science, artificial intelligence, business, or a related field is preferred.
Preferred candidates will have experience leading institutional AI, educational technology, or digital-transformation initiatives. Experience working with or reporting to institutional governance and policy bodies, such as data-governance or AI-governance committees, is also preferred.
Additional preferred experience includes leading organizational change associated with emerging technologies, developing institution-wide professional learning initiatives, supporting responsible AI adoption, and building partnerships that connect higher education with employer and community needs.
Opportunity and ImpactThis is an exciting opportunity for a forward-thinking leader to shape the strategy, infrastructure, and culture surrounding artificial intelligence at Indiana Tech. The Executive Director will help faculty, staff, and students develop confidence and competency in using AI, support the redesign of courses and academic programs, strengthen regional partnerships, and establish the systems needed for ethical, secure, and sustainable AI adoption.
Success will be reflected in the achievement of AIHE grant milestones; growth in faculty and staff AI readiness; the number of courses and programs redesigned with AI integration; regional partnership agreements and their workforce impact; compliance reviews completed without significant findings; faculty, staff, and student satisfaction; and the successful publication of the annual “State of AI at Indiana Tech” report.
Why Indiana Tech?Indiana Tech is a private, nonprofit university committed to career-focused education, innovation, collaboration, and student success. The Executive Director will join a mission-focused academic leadership team and have the opportunity to guide the responsible use of an emerging technology that will influence teaching, learning, workforce preparation, and institutional operations.
Ready to shape the future of responsible AI adoption in higher education? Apply today and help Indiana Tech build an innovative, ethical, and collaborative approach to artificial intelligence that prepares students, faculty, staff, and regional partners for what comes next.
**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. Director-level AI roles across all categories have a median of $272,275.
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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