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About This Role
Job no: 493449
Work type: Full-time
Location: Chester
Categories: Administrators
Widener University is currently seeking an AI Solutions Specialist. Reporting to the Vice President for Enrollment & Technology/Chief Information Officer, this newly created position will help the university’s administrative units put artificial intelligence to practical use, improving the speed and quality of the work that supports our students, faculty, and staff. The role will report directly to the Vice President while it is being established, then move into the Information Technology department.
This is a hands-on role. The AI Solutions Specialist will work with units such as Admissions, Student Financial Services, the Registrar, Finance, Human Resources, Information Technology Services, etc. to identify high-value opportunities, then personally configure, test, deploy, and monitor the tools that address them. It is also strategic: the position maintains a university-wide view of where AI is and should be used, recommends what the university should license, and builds the training that equips staff to leverage these new tools.
Qualified candidates will have both technological expertise and the interpersonal skills necessary to engage colleagues and campus stakeholders. Much of this work involves understanding how staff actually do their jobs and earning the trust required to manage change. The university is interested in what demonstrably works, not in adopting technology for its own sake. Duties and priorities will shift on an ongoing basis, so the successful candidate will bring comfort with ambiguity, a willingness to “figure it out,” and the autonomy to identify opportunities rather than wait for assignments.
This position is eligible for regular remote/flex work, and the university will consider fully remote candidates; some on-campus presence may be expected during onboarding and periodically thereafter. Widener is committed to fostering an environment in which faculty, staff, and students from a variety of backgrounds, cultures, and personal experiences are welcomed and can thrive.
DUTIES AND RESPONSIBILITIES (including, but not limited to):
Essential Duties:
- Partner with administrative units to identify work that is repetitive, slow, error-prone, or backlogged, and assess honestly whether AI is the right tool.
- Configure, test, and deploy AI solutions hands-on: prompts, custom assistants and agents, document-grounded chat and search over a unit’s own content, drafting and summarization workflows, and extraction of structured data from forms and correspondence.
- Connect AI capabilities to existing university systems using workflow automation tools so that outputs land where staff already work.
- Monitor deployed solutions on a regular basis reviewing output quality, tracking adoption, catching degradation, and adjusting configuration as content, processes, and vendor capabilities change. Document configurations, prompts, and known limitations so that deployments are supportable and repeatable.
- Define and report success measures for each deployment (time saved, turnaround time, error rates, volume handled) and recommend retiring or rebuilding what does not deliver.
- In partnership with others, design and deliver practical training workshops, office hours, and role-specific guides that raises baseline AI fluency and builds capable practitioners in business units.
- Evaluate new tools, assess vendor claims critically, maintain a roadmap of AI use across administrative units, and report progress and priorities to university leadership.
- Ensure that every deployment adheres to university policy and applicable requirements for data security and privacy, record retention, and appropriate use.
Secondary Responsibilities:
- Serve on division and campus-wide committees and participate in process improvement projects or strategic initiatives as assigned.
- Keep informed about developments in artificial intelligence, automation, and higher education technology, including relevant policies, regulations, and state and federal laws.
- Potential for modest supervisory responsibilities and/or collaboration opportunities with student employees or interns.
- Perform other job-related duties as assigned or directed to support departmental needs and align with the mission, values and goals of Enrollment & Technology and Widener University.
MINIMUM QUALIFICATIONS (education/training and experience required):
Required:
- Bachelor’s degree.
- Demonstrated hands-on experience building solutions with current generative AI tools (e.g., configuring custom assistants or agents, developing prompts for production use, or deploying document-grounded chat or retrieval over an organization’s own content).
- Working knowledge of at least one major enterprise AI platform (for example, Microsoft 365 Copilot, Google Gemini, ChatGPT Enterprise, or Anthropic Claude), including its administrative settings.
- Comfort working with data in spreadsheets and structured files, and an understanding of how systems exchange data through APIs.
- Ability to evaluate AI output critically recognizing hallucination, bias, and quiet failure and to build the checks that catch problems before end users do.
- Demonstrated ability to map a business process, identify where the friction actually lies, and design a change that staff will adopt.
- Excellent interpersonal and communication skills, written and verbal, including the ability to explain technical concepts to non-technical colleagues clearly and without condescension.
- Customer service orientation and ability to interact with a wide variety of individuals within a complex and dynamic environment.
- Excellent analytical, problem-solving, and organizational skills, with thoroughness and attention to detail.
- Ability to work independently, manage multiple projects, and prioritize among competing needs.
- Ability to identify gaps in one’s own knowledge and locate the resources to learn on one’s own, essential in a field changing this quickly.
Formal software development experience is not required; candidates who have built practical solutions through configuration rather than code are encouraged to apply.
Preferred:
- Master’s degree.
- Experience in higher education, or in another complex, decentralized organization.
- Experience delivering training or change management to non-technical staff.
- Familiarity with enterprise administrative systems such as Ellucian Colleague, Technolutions Slate, etc.
- Scripting ability (e.g., Python or JavaScript).
SPECIAL REQUIREMENTS:
- Occasional travel to the university’s campuses will be required.
- This position may require vacation restrictions during times of peak activity in the department.
- The employee must have the ability to occasionally lift/or move 10+ pounds.
- The employee will perform repetitive motion using computer mouse and keyboard to type.
Qualified candidates must submit resume and cover letter for consideration. Incomplete applications will not be considered. Only those selected to interview will be contacted.
All qualified applicants are encouraged to apply and will receive consideration for employment without regard to race, religion, color, national origin, age, sex, sexual orientation, disability status, or any other characteristic protected by applicable law. Widener University is committed to fostering an inclusive community in which faculty, staff, and students from a variety of backgrounds, cultures, and personal experiences are welcomed and can thrive. We are an equal opportunity employer and are committed to providing equal educational and employment opportunities for all persons without regard to age, color, national origin, race, religion, disability, veteran status, sex, sexual orientation, gender identity, genetic information, or status as a protected veteran.
Some positions may be eligible for a hybrid or remote work arrangement that may include a partially or fully remote work location, consistent with Widener’s Flexible Work policy.
U.S. work authorization is required.
Widener University, an independent, metropolitan, doctoral-intensive university, connects curricula to social issues through civic engagement. Dynamic teaching, active scholarship, personal attention and experiential learning are key components of the Widener Experience. Located in Chester, PA, Widener's main campus is nestled between Philadelphia, PA and Wilmington, DE, with Law Schools located in both Harrisburg and Wilmington.
EOE M/F/V/D
Advertised: 19 Oct 2026 Eastern Daylight Time
Applications close:
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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At widener 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 $212,040 based on 13,780 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 $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
widener University AI Hiring
widener University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chester, PA, US.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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