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About This Role
Job Description:
The purpose of the Data Architect - Staff role is to design, implement, and maintain both traditional and modern data infrastructures at the staff level. This involves combining expertise in relational databases, cloud technologies, and big data tools to create scalable, efficient, and secure data solutions. The role also includes developing strategies for integrating enterprise data, ensuring data quality, and optimizing data access across both cloud and on-premise systems We are committed to offering flexible work options where approved and stated in the job posting. However, we are currently not considering candidates who reside or plan to reside in the following states: California, Connecticut, Hawaii, Illinois, Massachusetts, Minnesota, New York, Pennsylvania, Rhode Island, Vermont, Virginia and Washington. Colorado for remote caregivers’ whose assigned Intermountain facility or service area is not based in Colorado.
Please note that a video interview through Microsoft Teams will be required as well as potential onsite interviews and meetings
Essential Functions
- Responsible for designing, implementing, and maintaining both traditional and modern data infrastructures at the staff level.
- Combines expertise in relational databases, cloud technologies, and big data tools to create scalable, efficient, and secure data solutions.
- Develops strategies for integrating enterprise data, ensuring data quality, and optimizing data access across both cloud and on-premise systems.
- Works on moderate to complex projects and is mentored by senior-level data architects/engineers to deliver robust data models, pipelines, and warehouses that meet organizational needs using technologies like SQL, Python, PySpark, and traditional RDBMS platforms
Skills
- Data architecture and design
- Cloud infrastructure and data solutions
- ETL/ELT development and data integration
- Database and performance optimization
- Data modeling and documentation
- Coding/Programming
- Data governance and security
- Collaboration and stakeholder management
Leadership and project management *
Qualifications
Minimum Qualifications
- Proficiency with SQL and experience with traditional RDBMS (e.g., Oracle, SQL Server, PostgreSQL).
- Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform for data architecture and storage solutions.
- Strong familiarity with programming languages such as Python and PySpark for data engineering tasks.
- Working experience with ETL/ELT processes and tools, including both traditional (e.g., SSIS, Informatica) and/or cloud-native solutions (e.g., Azure Data Factory, Databricks).
- Proficiency with Python or PySpark or R programming
- Excellent communication skills for collaborating with stakeholders and teams.
- Proficiency in Product Management, Project Management, or Program Management philosophies and methodologies, and capable of applying them to data architecture projects to ensure alignment with business goals and efficient execution.
Preferred Qualifications
- Design, build, and extend platform capabilities that enable enterprise data governance, including metadata management, lineage, stewardship, policy enforcement, and governance automation beyond vendor-provided functionality.
- Build and maintain integrations between governance, data, analytics, AI, and operational platforms so metadata, classifications, ownership, quality signals, and governance decisions remain synchronized across the enterprise ecosystem.
- Engineer the metadata, lineage, observability, and catalog capabilities of the Enterprise Data Platform, making enterprise data assets discoverable, understandable, traceable, and trusted for analytics, operations, and AI workloads.
- Automate governance controls within the platform, including ownership assignment, certification, review workflows, policy enforcement, exception management, and audit evidence collection to reduce manual effort and improve compliance.
- Apply AI, LLM, and agentic technologies to scale governance operations, including metadata generation, business glossary enrichment, data classification, stewardship recommendations, data quality monitoring, and intelligent remediation workflows.
- Establish and operate the engineering practices that make AI-enabled governance trustworthy, including grounding on authoritative metadata, evaluation frameworks, quality measurement, confidence thresholds, monitoring, and continuous improvement.
- Deliver secure and governed platform services that support analytics, AI agents, applications, and data products, ensuring enterprise standards for security, privacy, governance, and responsible AI are consistently applied.
- Build platform capabilities that increase the effectiveness and reach of data stewards and governance programs, including workflow management, stewardship tooling, governance coverage reporting, operational dashboards, and scalability mechanisms.
Partner with data architecture, governance, analytics, engineering, cybersecurity, and vendor teams to translate governance objectives into sustainable platform capabilities, owning the full lifecycle from design and deployment through monitoring, support, and continuous enhancement. *
Physical Requirements
- Remain sitting or standing for long periods of time to perform work on a computer, telephone, or other equipment.
Location:
Transformation Center Work City:
Murray Work State:
Utah Scheduled Weekly Hours:
40 The hourly range for this position is listed below. Actual hourly rate dependent upon experience.
$44.36 - $69.85
We care about your well-being – mind, body, and spirit – which is why we provide our caregivers a generous benefits package that covers a wide range of programs to foster a sustainable culture of wellness that encompasses living healthy, happy, secure, connected, and engaged.
By applying for a position with Intermountain, I acknowledge that I will comply with all applicable Intermountain policies and expectations. If applying for a remote or hybrid role, this includes remote work expectations related to confidentiality, information security, work schedules, conflicts of interest, and use of company equipment. I further acknowledge that outside employment or activities may not interfere with job responsibilities or create a conflict of interest with Intermountain. Actual or reasonably perceived conflicts may be grounds for disqualification from consideration or, if hired, corrective action up to and including termination of employment.
Intermountain Health is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status.
At Intermountain Health, we use the artificial intelligence ("AI") platform, HiredScore to improve your job application experience. HiredScore helps match your skills and experiences to the best jobs for you. While HiredScore assists in reviewing applications, all final decisions are made by Intermountain personnel to ensure fairness. We protect your privacy and follow strict data protection rules. Your information is safe and used only for recruitment. Thank you for considering a career with us and experiencing our AI-enhanced recruitment process.
All positions subject to close without notice.
Salary Context
This $91K-$143K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Intermountain Health, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($117K) sits 45% below the category median. Disclosed range: $91K to $143K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Intermountain Health AI Hiring
Intermountain Health has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Murray, UT, US. Compensation range: $143K - $143K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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