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About This Role
Engineering a world of possibilities
The AI Solutions Architect drives the rapid development, integration, and scaling of AI\-powered solutions that align with institutional goals and regulatory requirements. This role bridges the gap between university needs and technical capabilities, translating emerging AI technologies, including agentic AI, into secure, ethical, and user\-centered tools that enhance academic, administrative, and operational outcomes. PLEASE NOTE: This position is not eligible for visa sponsorship now, nor in the future.
Working closely with IT and business partners, the AI Solutions Architect ensures that AI initiatives are implemented responsibly, comply with Mine’s AI and data governance standards, and align with the university’s strategic direction. This is an individual contributor role with high visibility and impact, requiring both strategic vision and hands\-on technical execution to create and maintain AI agents, perform system and data integrations, and validate outputs for accuracy and reliability.
This position is currently hybrid, and requires regular commuting to campus.
Primary Responsibilities:
AI Integration, Enablement \& Governance
- Embed AI tools into university systems and business processes to ensure successful adoption.
- Integrate Microsoft, Google, and other approved AI services into workflows.
- Support onboarding, training, and change management for responsible AI use.
- Ensure compliance with Mines data governance and AI policies.
- Contribute to AI governance frameworks, including model transparency and risk mitigation.
AI Solutions \& Strategy
- Develops, deploys, and aligns AI solutions with institutional goals and long\-term value.
- Identify and fast\-track opportunities for AI\-driven innovation that enhance academic programs, student experiences, and operational efficiency.
- Accelerate the full lifecycle of AI product development, from discovery and prototyping to deployment and iteration.
- Understand product roadmaps and assess features, technical feasibility, and overall institutional value.
- Translate complex AI concepts (e.g., large language models, prompt engineering, RAG, AIOps) into actionable product strategies.
- Develop and track key performance indicators (KPIs) and user feedback to measure the success and impact of AI initiatives.
Stakeholder Collaboration \& AI Literacy
- Emphasizes cross\-functional engagement and building AI fluency across the institution.
- Establish relationships and partner with academic, administrative, and IT stakeholders to implement requirements and align AI solutions with institutional goals.
- Serve as a liaison between technical teams and end users to ensure clear communication and shared understanding.
- Contribute to AI literacy initiatives by developing documentation, training resources, and awareness campaigns for faculty, staff, and students.
Minimum Qualifications
- Bachelor's degree from a four\-year college or university in computer science, data science, information systems, engineering, or related field. Individuals without a degree may be considered if they demonstrate possession of substantially the same knowledge level as found in a degree but have attained the advanced knowledge through a combination of work experience and intellectual instruction
- 3\+ years of experience in one or more of the following areas:
+ Product ownership in a technology\-driven environment
+ Artificial Intelligence (AI) architecture or implementation (Can include Machine Learning (ML) development or implementation)
+ Business analysis or digital transformation
+ Data analytics, data governance, or data strategy
+ Technology enablement or IT project management
- Strong understanding of AI/ML concepts, including generative AI, prompt engineering, and model evaluation
- Ability to translate complex technical concepts into actionable strategies for diverse stakeholders
- Familiarity with cloud\-based AI platforms (e.g., Microsoft Azure, Google Cloud, AWS)
- Knowledge of data privacy and compliance frameworks (e.g., FERPA, HIPAA, GDPR)
- Excellent communication, collaboration, and stakeholder engagement skills
- Demonstrated ability to lead cross\-functional initiatives and drive organizational change
- Eagerness to learn Agentic AI skills that will immediately be put to use
- Self\-starter with a high degree of initiative, accountability, and adaptability
Preferred Qualifications:
- Master’s degree in a related field
- Experience managing AI or data\-driven products in complex, regulated environments
- Experience in higher education, research institutions, or public sector organizations
- Hands\-on experience with AI/ML development processes, including model evaluation, prompt design, and iterative testing
- Experience contributing to or leading AI governance initiatives, including model transparency and risk mitigation
- Certified Scrum Product Owner (CSPO)
- Information Technology Infrastructure Library (ITIL) for IT Service Management
- Project Management Professional (PMP)
- Microsoft Certified: Azure AI Engineer Associate
- Google Cloud Professional Machine Learning Engineer
- Certified Analytics Professional (CAP)
- Familiarity with AIOps tools and practices for continuous integration and monitoring of AI systems
- Strong knowledge of Microsoft 365 Copilot, Google Gemini Enterprise, and other enterprise AI tools
- Proficiency with Agile methodologies and product management tools
Salary and Benefits
$100,000 \- $110,400 annual salary
Mines takes into consideration a combination of candidate’s education, training and experience as well as the position’s scope and complexity, the discretion and latitude required in the role, work location, and external market and internal value when determining a salary level for potential new employees.
Colorado School of Mines offers a robust portfolio of benefits for all employees. For this role, that includes:
- Flexible health and dental care options
- Generous sick/vacation time: 13 paid holidays per year – including a week\-long winter break for entire campus.
- Fully vested retirement plan on first day of employment, with generous employer contribution
- Tuition benefits (6 credits per year for employees, 50 percent discount for dependents)
- Free RTD Ecopass
All Mines employees also have access to discount programs through the State of Colorado and free tickets for Mines Athletics home games, as well as access to the state of the art Recreation Center (fitness classes and training, swimming pool and more) and equipment rentals through the Outdoor Rec Center. We are proud to have recently opened an on campus daycare center. For more details about benefits at Mines, visit mines.edu/human\-resources/benefits.
How to Apply
Complete an online application (personal information, demographic information, veteran status)
- Upload a resume or CV
- Upload a cover letter
Visa sponsorship is not available for this position, and will not be available for this position in the future.
This posting will be used to fill more than one vacancy based on business needs.
References will not be contacted until later in the selection process and you will be informed before that contact is made.
Application review will begin July 26, 2026, but may close prior to that date. For best consideration, apply early.
Successful Completion of a Background Investigation is Required for this Position.
Additional Information and Reasonable Accommodation Requests
It is the intent of Mines to comply with the applicable requirements of the Americans with Disabilities Act and the Americans with Disabilities Act Amendments Act of 2008, and their implementation rules and regulations, in support of equal opportunities for qualified applicants with disabilities. To meet this goal, Mines will make reasonable accommodations during the employment selection process and within our working environment.
If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to access job openings or apply for a job on our site as a result of your disability. You can request a reasonable accommodation by contacting our Human Resources team at hr@mines.edu or 303\.273\.3250 for assistance.
Successful completion of a background investigation is required for this position.
Equal Opportunity
Colorado School of Mines is committed to equal opportunity for all persons. Mines does not discriminate on the basis of age, sex, gender (including gender identity and gender expression), ancestry, creed, marital status, race, ethnicity, religion, national origin, disability, sexual orientation, genetic information, veteran status or current military service. Further, Mines does not retaliate against community members for filing complaints regarding or implicating any of these protected statuses.
Mines’ commitment to nondiscrimination, equal opportunity and equal access is reflected in the administration of its policies, procedures, programs and activities and in its efforts to achieve a talented student body and workforce.
Through its policies, procedures and resources, Mines complies with federal law, Colorado state law, administrative regulations, executive orders and other legal requirements to prevent discrimination (including harassment or retaliation) within the Mines campus community and to address potential allegations of inequality or concerns for safety.
*Colorado's premier engineering and applied science university for 150 years and counting*
Salary Context
This $100K-$110K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At COLORADO SCHOOL OF MINES, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($105K) sits 52% below the category median. Disclosed range: $100K to $110K.
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.
COLORADO SCHOOL OF MINES AI Hiring
COLORADO SCHOOL OF MINES has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Golden, CO, US. Compensation range: $110K - $110K.
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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