Interested in this AI/ML Engineer role at Ensign College?
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AI Solutions Manager
Reports to: Chief Information Officer
Starting at $135,000\.00 annually.
A higher starting salary may be offered based on qualifications and experience.
Primary Job Responsibilities:
The primary job functions are: Operational AI strategy and opportunity identification, business process analysis and solution design, project management and implementation coordination, AI tool evaluation and responsible use, training, and change management.
- Lead the identification, planning, and provide project management for the implementation of AI\-enabled solutions across the college.
- Partner with campus leaders to improve business processes through AI, automation, and workflow redesign.
- Manage AI projects from discovery through implementation, adoption, and continuous improvement.
- Facilitate cross\-functional collaboration among IT, academic, and administrative departments.
- Evaluate emerging AI technologies and recommend solutions that align with institutional priorities.
- Promote responsible AI practices, including data privacy, security, accessibility, and human oversight.
- Develop training, resources, and change management strategies to support successful adoption.
- Chair the college's AI Subcommittee and help shape institutional AI governance and priorities.
Required Qualifications:
- Bachelor's degree in information systems, business administration, project management, instructional technology, operations management, data analytics, or a related field.
- Three or more years of experience in business analysis, project management, process improvement, technology implementation, digital transformation, or related work.
- Working knowledge of generative AI tools, AI\-enabled business applications, automation platforms, and practical AI use cases in organizational settings.
- Demonstrated ability to facilitate meetings, gather requirements, document business processes, and translate user needs into practical solution designs.
- Strong verbal, written, and interpersonal communication skills, including the ability to work effectively with technical and non\-technical audiences.
- Demonstrated project management skills, including scoping, planning, coordinating stakeholders, tracking deliverables, managing risks, and supporting successful implementation.
- Sound judgment regarding data privacy, information security, ethical AI use, accessibility, and appropriate human oversight.
- Ability to learn new technologies quickly and evaluate tools based on institutional need, risk, usability, cost, and sustainability.
Preferred Qualifications:
- Experience working in higher education, student services, enrollment, financial aid, advising, finance, human resources, facilities, compliance, or other college operational areas.
- Experience with business process re\-engineering, Lean, Six Sigma, continuous improvement, service design, or organizational change management.
- Experience implementing AI, automation, CRM, ERP, workflow, chatbot, knowledge management, reporting, analytics, or productivity tools.
- Project management certification or training, such as PMP, CAPM, Agile, Scrum, Lean Six Sigma, Prosci, or similar.
- Familiarity with FERPA, data governance, accessibility, cybersecurity, responsible AI practices, and higher education compliance expectations.
Requirements for Employment Eligibility:
- Membership in The Church of Jesus Christ of Latter\-day Saints.
- Worthiness to hold and be worthy to hold a current temple recommend.
- Receive clearance from the Church Educational System Ecclesiastical Clearance Office.
- Strict adherence to company policies and procedures.
- Commitment to bring about the purposes of Ensign College.
- Reside in Utah and able to work in person at our downtown Salt Lake City campus.
Ensign College is an equal opportunity employer and does not unlawfully discriminate in its hiring or employment practices on the basis of race, color, national or ethnic origin, sex (including pregnancy), age, disability, genetic information, or veteran's status. As an educational institution owned and operated by The Church of Jesus Christ of Latter\-day Saints, Ensign College gives lawful hiring preference to qualified, faithful members of the Church who are in good standing.
Job Type: Full\-time
Pay: From $135,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Flexible spending account
- Health insurance
- Health savings account
- Life insurance
- Paid time off
- Parental leave
- Professional development assistance
- Relocation assistance
- Retirement plan
- Tuition reimbursement
- Vision insurance
Work Location: In person
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ensign College, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Ensign College AI Hiring
Ensign College has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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