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About This Role
Job Description:
Ryan Companies US, Inc. has an immediate career opportunity for an Artificial Intelligence Engineer to join our growing Data and Artificial Intelligence team. Reporting directly to the Director of AI, this high-impact role operates on the front lines of Ryan’s construction operations. This is an IN-OFFICE role for our Minneapolis headquarters office, 4 days per week, 1 day work-from-home. It will require weekday travel for likely 4-5 days per month to construction sites mostly in the Midwest.
Unlike traditional centralized engineering roles, the Forward Deployed AI Engineer will be heavily embedded with our Construction project teams; partnering side-by-side with Project Managers, Superintendents, Project Engineers, and Field Operations. You will act as the technical bridge between enterprise AI capabilities and day-to-day jobsite workflows, rapidly prototyping, integrating, and deploying custom AI solutions directly into field operations.
This role requires strong hands-on AI coding proficiency, deep cloud expertise on Google Cloud Platform (GCP), a product-focused mindset, and a willingness to operate on active project sites to drive tangible business value in construction delivery.
With more than 88 years in business and 17 offices (and growing!), Ryan offers comprehensive commercial real estate solutions to customers across the US. We bring a collaborative, relationship-driven approach to delivering spaces where people thrive and businesses succeed. Join a company with an outstanding culture and competitive benefits. At Ryan, we don’t just build buildings – we build stories.
What you can expect to do:
- Field-Embedded AI Deployment: Embed directly into construction project teams to observe, diagnose, and solve operational friction points on jobsites using AI and automation.
- Agentic Rapid Prototyping: Leverage state-of-the-art agentic coding environments (e.g., Cursor, Claude Code, Codex) to build, test, and iterate on functional prototypes with velocity.
- Software & AI Engineering: Write scalable, robust, and maintainable code to build tailored applications, agentic workflows, and field technology tools.
- Cloud Development: Design, configure, and maintain production solutions within Google Cloud Platform (GCP), leveraging Agent Engine (Vertex AI), Cloud Run, BigQuery, and Cloud Storage, etc.
- RAG and Agent Development: Build and deploy Retrieval-Augmented Generation (RAG) applications and autonomous agents to query complex construction documents (drawings, specs, submittals, RFIs, change orders).
- Construction Data Integration: Connect solutions, models and pipelines into existing construction ecosystems (such as Procore, Autodesk Construction Cloud, ERPs, and document repositories).
- Feedback Loop and Scale: Serve as the critical feedback loop between construction site realities and the centralized AI team, turning field-tested prototypes into scalable enterprise solutions.
- Culture Building: Actively participate in fostering Ryan’s data and AI culture through promoting data-driven decision-making.
What we expect you to have:
- 3+ years of hands-on experience building and deploying generative AI, machine learning or advanced software solutions in enterprise or field environments.
- Advanced proficiency and hands-on workflow mastery utilizing agentic coding environments and AI harnesses (such as Cursor, Claude Code, Codex, or comparable tools) to accelerate software architecture, coding, testing, and system delivery.
- Proven experience architecting and deploying application and agent solutions specifically on Google Cloud Platform (GCP), including Agent Engine (Vertex AI), Cloud Run, Cloud Composer/Dataflow, and IAM security models.
- Demonstrated experience developing custom RAG pipelines, fine-tuning LLMs, working with vector databases, and orchestrating multi-agent systems
- Strong coding proficiency in Python, SQL, TypeScript/JavaScript; solid grasp of RESTful APIs, data serialization, and Git workflows.
- Ability and willingness to travel regularly to active construction sites, participate in job site meetings, and understand field operations.
- Bachelor’s degree in Computer Science, Data Science, Software Engineering, Construction Engineering, or equivalent practical experience.
To be Successful
- Strong problem-solving skills and the ability to communicate effectively with both technical and non-technical audiences.
- Eagerness to learn the business of construction to create impactful and valuable AI solutions.
- Ability to work collaboratively in a team environment with diverse skill sets and work styles.
- Resourceful, entrepreneurial, and innovative mindset, with a drive to tackle ambiguous challenges effectively.
- Commitment to staying updated on AI trends and leveraging them to deliver business value.
- Understanding of ethical and responsible AI principles and practices
Preferred Experience
- Prior exposure to Architecture, Engineering, and Construction (AEC), commercial real estate, or industrial operations.
- Familiarity with construction tools and APIs (Procore, Autodesk BIM 360 / ACC, Bluebeam, DroneDeploy).
- Experience designing conversational interfaces or automated field data capture workflows (voice-to-text, field forms)
Compensation:
The base pay range is $100,000 - $130,000/Annually. The salary may vary within the anticipated range based on factors such as the ultimate location of the position and the selected candidate’s knowledge, skills, and abilities. Position may be eligible for additional compensation that may include commission and/or an incentive program.
Eligibility:
Position requires verification of employment to work in the U.S.
Benefits:
- Competitive Salary
- Medical, Dental and Vision Benefits
- Retirement and Savings Benefits
- Flexible Spending and Health Savings Accounts
- Life Insurance
- Short-Term and Long-Term Disability
- Educational Assistance
- Paid Time Off (PTO)
- Employee Assistance and Wellness Programs
- Family Planning & Parenting Benefits
- Employee Discount Programs
- Pet insurance
- Charitable Donation Matching
- Volunteer Time Off (VTO)
- Employee Resource Groups (ERGs)
Disclaimer: Eligibility may vary based on factors such as role, hours worked, employment status, length of service, location, and other considerations. Detailed information will be shared with eligible candidates during the hiring process, and the official terms and conditions will be outlined in each individual offer document.
Ryan Companies is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Notice to Candidates:
Please be advised that initial outreach regarding employment at Ryan Companies will only occur via email from the domain @ryancompanies.com email address. If you recieve communication from someone you believe is impersonating Ryan Companies, please report it to us at [email protected]
Non-Solicitation Notice to Recruitment Agencies:
Ryan Companies kindly requests that recruitment agencies and third-party recruiters do not submit unsolicited resumes or candidate information to any Ryan Companies employee or office. Ryan Companies will not be responsible for any fees or expenses associated with unsolicited submissions. If recruitment services are required, we will reach out directly to agencies on our approved vendor list. We appreciate your understanding and cooperation.
Salary Context
This $100K-$130K 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 Ryan Companies US, Inc., 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 ($115K) sits 46% below the category median. Disclosed range: $100K to $130K.
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.
Ryan Companies US, Inc. AI Hiring
Ryan Companies US, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Des Moines, IA, US. Compensation range: $130K - $130K.
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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