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About This Role
Job Title
Senior AI Solutions Engineer Department
IT Project Management Worker Type
Regular Pay Type
Salary Position Salary Minimum
$100,000 Position Salary Maximum
$120,000 Salary will be commensurate with the level of the position, education, and experience.
Scheduled Weekly Hours
40 Benefit Eligible
Yes Screening Date
2026-11-02 Job Description Summary
The Senior AI Solutions Engineer advances the practical, responsible adoption of artificial intelligence across Miami University by connecting institutional needs with shared AI services, technical teams, and implementation partners.
This role combines hands-on technical fluency with consultation, solution delivery, and relationship building. You will help design and operate shared AI capabilities, guide academic and administrative units from ideas through pilots and sustainable solutions, and establish repeatable technical and operational patterns that allow AI services to scale securely and effectively. Working across organizational boundaries, you will coordinate with security, architecture, infrastructure, service owners, academic partners, and institutional AI groups while exercising significant independent judgment in a rapidly changing environment.
The position contributes implementation expertise to institutional AI governance but is primarily accountable for delivering usable services, enabling partners, and turning emerging AI capabilities into practical outcomes. Job Description
Duties
- Lead the design, implementation, rollout, and continuous improvement of shared University AI services within the perspective and purview of the Information Technology Services Division, including managed access to enterprise generative AI platforms, service gateways, account and domain administration, and related operating practices.
- Serve as a consultative point of entry for academic and administrative units exploring AI-enabled solutions; clarify needs, assess feasibility, value, risk, and supportability, and recommend practical paths forward.
- Prototype, configure, and integrate AI-enabled solutions when appropriate, and coordinate with technical teams and vendors to move promising pilots toward stable, supportable services.
- Partner with units responsible for information security, legal, procurement, accessibility, architecture, data governance, and service owners to translate institutional requirements into practical technical and operational controls.
- Develop reusable processes, service definitions, intake processes, financial/chrage-back models, documentation, and guardrails for common AI use cases.
- Build and maintain awareness of significant AI initiatives across University divisions; connect teams, and encourage consistent approaches.
- Collaborate with the University AI Center of Excellence, AI fellows, IT leaders, and other campus partners to support coordinated adoption and help build communities of practice for people applying AI in their work.
- Create the framework to evaluate emerging AI technologies, and apply that framework to models, platforms, and vendors; communicate capabilities, limitations, tradeoffs, and recommendations to technical and non-technical audiences.
- Establish and monitor measures for service adoption, reliability, support needs, and outcomes, and use those measures to improve AI services and consulting practices.
- Contribute implementation experience and technical perspective to institutional AI governance efforts and help translate approved guidance into workable service and technology practices.
Develop and maintain business cases, including detailed financial evaluations, of proposed solutions. *
Minimum Qualifications
Bachelor’s degree by date of hire and 6+ years of related professional technical experience.
Or
Relevant Master's degree by date of hire AND 4+ years of related professional technical experience.
Experience may come from roles such as senior business analyst, technical lead, solutions architect, or similar positions.
Knowledge, Skills, and Abilities (KSAs)
- Ability to rapidly learn, evaluate, and apply emerging artificial intelligence technologies in a practical enterprise environment.
- Ability to build credibility, partnerships, and alignment across organizational boundaries without relying on formal authority.
- Ability to translate ambiguous business, academic, or operational needs into clear technical options and supportable solutions.
- Ability to move cross-functional initiatives from idea through pilot and enterprise implementation with a high degree of independence and sound judgment.
- Working knowledge of generative AI concepts, large language models, APIs and integrations, identity and access management, data handling, and common security and privacy considerations.
- Ability to communicate AI capabilities, limitations, risks, and tradeoffs clearly to both technical and non-technical audiences.
- Ability to balance innovation and speed with security, privacy, accessibility, reliability, maintainability, and institutional requirements.
- Ability to prioritize and manage multiple concurrent initiatives in a fast-changing environment.
- Ability to facilitate consultations, demonstrations, workshops, and collaborative working sessions.
Ability to create clear documentation, reusable patterns, and service practices that improve consistency and organizational learning. *
Preferred Qualifications
- Experience implementing, administering, or supporting enterprise generative AI services such as ChatGPT, Claude, Microsoft Copilot, Google Gemini, Azure OpenAI, AWS Bedrock, or similar platforms.
- Experience with large language model APIs and common AI solution patterns such as retrieval-augmented generation (RAG), agents, embeddings, vector search, or workflow automation.
- Experience prototyping or integrating technology solutions using Python, JavaScript, APIs, low-code platforms, or similar tools.
- Experience with enterprise identity and access management, SaaS/cloud administration, and information security practices.
- Experience in technical consulting, evaluating technology vendors, technology service ownership, product management, or solution delivery across multiple stakeholder groups.
- Experience building business cases, piloting emerging services, and moving successful pilots into sustainable operational support.
- Experience facilitating communities of practice, training, workshops, or other peer-learning approaches.
Experience working in higher education or another complex, decentralized organization. *
Benefits
At Miami University, we value the well-being, growth, and success of every member of our community. Through our comprehensive benefits and wellness programs, we support physical, financial, and professional well-being so you can thrive personally and professionally.
We offer a robust benefits package that includes a variety of healthcare options — including medical, dental, vision, life, and long-term disability insurance — as part of our commitment to a culture of health and wellness. Benefit-eligible employees also have access to wellness resources, biometric health screenings, and programs that support fitness, nutrition, and health coaching.
Miami provides generous paid time off, including vacation, sick leave, parental leave, and paid holidays, so you can maintain a healthy work–life balance. Full-time employees are immediately eligible for tuition fee waivers, with eligibility for spouses and dependents after a set period of continuous employment, and you can pursue lifelong learning while you work.
We also offer retirement resources, including alternative and supplemental retirement plans, to help you plan for long-term financial well-being.
Our commitment to your well-being extends beyond traditional benefits — with programs that support mental, physical, and financial health, and opportunities to engage in the vibrant academic and cultural life on campus.
Additional Position Information (if applicable)
Required Application Documents
Professional résumé and cover letter Special Instructions (if applicable)
N/A Additional Information
A criminal background check is required. All campuses are smoke- and tobacco-free campuses.
This organization participates in E-Verify.
Remote Work
For positions that are approved for remote work: Remote work is not a right, it is a work arrangement that can be modified or revoked by Miami University at any time for any reason, including the convenience of the University.
Reasonable Accommodations
Requests for reasonable accommodations for disabilities related to employment should be directed to [email protected] or 513-529-3560. Questions and follow-ups regarding requests should also be directed here.
Miami University Values Statement
Miami University is a scholarly community whose members believe that a liberal education is grounded in qualities of character as well as of intellect. We respect the dignity of other persons, the rights and property of others, and the right of others to hold and express disparate beliefs. We believe in honesty, integrity, and the importance of moral conduct. We defend the freedom of inquiry that is the heart of learning and combine that freedom with the exercise of judgment and the acceptance of personal responsibility.
For more information on Miami University’s mission and core values, please visit the Mission and Core Values webpage.
Equal Opportunity/Affirmative Action Statement
Miami University, an Equal Opportunity/Affirmative Action employer, encourages applications from protected veterans and individuals with disabilities. Miami University prohibits harassment, discrimination and retaliation on the basis of age (40 years or older), color, disability, gender identity or expression, genetic information, military status, national origin (ancestry), pregnancy, race, religion, sex/gender, status as a parent or foster parent, sexual orientation, or protected veteran status in its application and admission processes, educational programs and activities, facilities, programs or employment practices. Requests for reasonable accommodations for disabilities related to employment should be directed to [email protected] or 513-529-3560.
Clery Act
As part of the University’s commitment to maintaining a healthy and safe living, learning, and working environment, we encourage you to read Miami University’s Annual Security & Fire Safety Report at: http://www.MiamiOH.edu/campus-safety/annual-report/index.html , which contains information about campus safety, crime statistics, and our drug and alcohol abuse and prevention program designed to prevent the unlawful possession, use, and distribution of drugs and alcohol on campus and at university events and activities. This report also contains information on programs and policies designed to prevent and address sexual violence, domestic violence, dating violence, and stalking. Each year, email notification of this website is made to all faculty, staff, and enrolled students. Written notification is also provided to prospective students and employees. Hard copies of the Annual Security & Fire Safety Report may be obtained from the Miami University Police Department at 513-529-2223.
Labor Law Posters for Applicants
Please visit our Labor Law Posters webpage to access all relevant and applicable labor law information.
Salary Context
This $100K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 roles with salary data).
View full AI/ML Engineer salary data →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 Miami 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($110K) sits 48% below the category median. Disclosed range: $100K to $120K.
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.
Miami University AI Hiring
Miami University has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Oxford, OH, US. Compensation range: $120K - $120K.
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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