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About This Role
MN, United States \| HQ Business Support
AI Platform Engineer
SUMMARYThe AI Platform Engineer designs, builds, and maintains the infrastructure that powers Mortenson’s AI solutions. This role ensures scalable, secure, and high\-performing AI platforms, enabling rapid experimentation and reliable deployment of models. It is critical to Mortenson’s 2026 priorities: AI readiness, governed delivery, and enterprise\-scale adoption.
Hiring Locations: Minneapolis, MNPlease make note:* Visa sponsorship is not offered for this position.
Our postings are typically open a minimum of 5 days and an average of 44 days.
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RESPONSIBILITIES:Platform Architecture \& Scalability
- Design and implement AI platform architecture that supports model training, deployment, and monitoring at scale.
- Optimize compute, storage, and networking for performance and cost efficiency.
Infrastructure Automation \& CI/CD
- Build automated pipelines for model deployment and infrastructure provisioning.
- Ensure seamless integration with Mortenson’s enterprise systems and Microsoft Fabric\-based data mesh.
Security \& Governance
- Implement security best practices for AI platforms, including identity management, encryption, and compliance monitoring.
- Ensure adherence to Mortenson’s AI governance standards (privacy, security, ethical use).
Performance Monitoring \& Reliability
- Establish observability for AI workloads (metrics, logs, alerts).
- Maintain 99\.9% uptime and rapid recovery for critical AI services.
Collaboration \& Support
- Partner with AI Engineers, Cloud Engineers, Data Scientists, and Operations teams to enable efficient workflows.
- Provide technical documentation and guidance for platform usage and best practices.
QUALIFICATIONSRequired:
- Bachelor’s degree in Computer Science, Engineering, or related discipline and/ or equivalent experience
- 3\+ years in platform engineering, DevOps, and/or AI/ML infrastructure roles
- Hands\-on experience with cloud platforms (Azure preferred), containerization (Docker/Kubernetes), and CI/CD tools
- Strong understanding of MLOps practices and distributed systems.
Preferred:
- Experience in AEC/construction technology environments.
- Familiarity with Microsoft Fabric and enterprise data integration.
- Knowledge of AI governance frameworks and security best practices.
A few benefits offered include:*(for Non\-Craft \& Non\-Union Craft working 25\+ hours / week)** Medical and prescription drug plans that includes no additional cost vision coverage
- Dental plan
- 401k retirement plan with a generous Mortenson match
- Paid time off, holidays, and other paid leaves
- Employer paid Life, AD\&D, and disability insurance
- No\-Cost mental health tool and concierge with extensive work\-life resources
- Tuition reimbursement
- Adoption Assistance
Gym Membership Discount Program
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The base pay range for this role is $85,300 – $128,000 and is located in MN (Actual range is higher for the following office locations: Denver, CO and Chicago, IL – 5%, Seattle, WA, and Portland, OR – 10%, Washington, D.C. – 12\.5%).
Base pay is positioned within the range based on several factors including an individual’s knowledge, skills, and experience, with consideration given to internal equity. This position is eligible for Mortenson’s incentive plan.
\#LI\-JY1
ABOUT MORTENSONAs a top builder, developer, and EPC (Engineering, Procurement, and Construction), our expertise spans markets like sports, renewable energy, data centers, healthcare, and more. We are builders at heart, working to ensure the built environment has a lasting positive impact.
Let’s Redefine Possible®
Equal Employment OpportunityYour uniqueness brings new and creative perspectives to the team. Mortenson is committed to providing equal opportunities of employment (EOE) to all individuals, regardless of your race, religion, gender, national origin, age, veteran status, disability, marital status or any other legally protected category.
Other Items to Note* Mortenson reserves the right to hire any individual without legal or financial obligation on unwanted solicitations. *No agency emails, calls, or solicitations are accepted* without a valid agreement.
- Must be currently legally authorized to work in the U.S. without sponsorship for employment visa status (e.g., H1B status, 0\-1, TN, CPT, OPT, etc.). We are unable to sponsor or take over sponsorship of an employment Visa at this time.
Salary Context
This $85K-$128K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Mortenson Construction, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($106K) sits 50% below the category median. Disclosed range: $85K to $128K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Mortenson Construction AI Hiring
Mortenson Construction has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in MN, US. Compensation range: $128K - $128K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 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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