Interested in this AI/ML Engineer role at Stantec?
Apply Now →Skills & Technologies
About This Role
"Stantec Buildings is on a mission to become the world's leading integrated design practice. Our architects, engineers, interior designers, sustainability specialists, and technologists are passionate about the power of design. We take an innovative, collaborative approach to projects, sharing a common belief that sustainable design for the built environment can make the world a better place for future generations.
Your Opportunity:
Join Buildings Digital Practice and help accelerate the adoption of artificial intelligence, automation, and digital solutions across the Buildings business. The AI & Automation Solutions Specialist is a Hybrid / Remote optional position and responsible for identifying opportunities where AI, Microsoft Copilot, Power Platform, and intelligent automation can improve business operations, project delivery, practitioner productivity, and decision-making. Working across Buildings leadership, business centers, project teams, Digital Practice, IT, and other stakeholders, this role helps transform ideas into practical, scalable, and measurable solutions. This position combines AI enablement, solution development, automation, training, and adoption activities while supporting Buildings' strategic focus on digital transformation and AI-enabled delivery.
Your Key Responsibilities:
AI & Copilot Enablement
- Champion the adoption and practical use of Microsoft Copilot and approved AI technologies across Buildings.
- Partner with business stakeholders to identify, evaluate, and prioritize high-value AI opportunities.
- Develop and share prompt libraries, templates, examples, and best practices.
- Deliver demonstrations, workshops, office hours, and learning sessions for users at varying levels of AI maturity.
- Coach teams on effective prompting, responsible AI practices, and output validation.
- Support implementation of Buildings AI initiatives and strategic roadmap activities.
- Facilitate knowledge sharing, success stories, lessons learned, and adoption across regions and business centers.
Power Platform & Solution Development
- Design, develop, and deploy business solutions using: - Power Apps
- Power Automate
- Power BI
- Copilot Studio
- Build automation workflows that reduce manual effort and improve consistency.
- Create reusable applications, templates, and automation frameworks.
- Support integrations between Microsoft 365, SharePoint, Teams, and approved enterprise platforms.
- Prototype solutions rapidly and scale successful implementations across the practice.
- Partner with stakeholders to translate business needs into practical digital solutions.
AI Agents & Intelligent Automation
- Design, build, test, and deploy AI agents using Copilot Studio and other approved platforms.
- Evaluate opportunities for AI agents and intelligent workflows within Buildings business processes.
- Develop agent instructions, knowledge sources, actions, and automation workflows.
- Support governance, testing, deployment, and lifecycle management of AI-enabled solutions.
- Collaborate with IT and Digital Practice teams to ensure solutions align with security, privacy, and governance requirements.
- Establish standards and best practices for AI-enabled automation solutions.
Process Improvement & Digital Transformation
- Assess existing workflows and identify opportunities to improve efficiency using AI and automation.
- Reduce manual effort through automation and intelligent business processes.
- Support digital transformation initiatives across Buildings.
- Collaborate with stakeholders to understand challenges, document opportunities, and recommend practical solutions.
- Help convert successful local solutions into scalable enterprise practices.
Adoption, Training & Community Development
- Support and coordinate Buildings AI Champions and Ambassador programs.
- Deliver onboarding, training, coaching, and support resources.
- Create quick-start guides, learning materials, videos, and self-service resources.
- Foster communities of practice focused on AI, Copilot, automation, and digital innovation.
- Gather user feedback and identify opportunities to improve adoption and effectiveness.
- Promote responsible, secure, and effective use of AI technologies.
Analytics & Value Realization
- Monitor AI, Copilot, and Power Platform adoption and usage metrics.
- Work with the data team to create dashboards and reporting using Power BI.
- Measure and communicate business impact, including: - Productivity improvements
- Time savings
- Process efficiencies
- Adoption rates
- Return on investment
- Support business cases and recommendations for continued investment in digital solutions.
- Identify trends and opportunities for ongoing improvement.
Technical & Digital Skills
- Strong understanding of Microsoft 365 solutions and ecosystem.
- Hands-on experience with: - Microsoft Copilot
- Copilot Studio
- Power Automate
- Power Apps
- Power BI
- SharePoint
- Microsoft Teams
- Experience building automation workflows and business solutions.
- Knowledge of AI technologies, large language models, and emerging AI platforms.
- Ability to assess AI outputs for accuracy, relevance, completeness, and business suitability.
- Understanding of AI governance, privacy, security, and responsible use practices.
Professional Skills
- Strong stakeholder engagement and relationship-building skills.
- Ability to communicate effectively with both technical and non-technical audiences.
- Strong facilitation, presentation, and training skills.
- Demonstrated problem-solving and analytical abilities.
- Ability to translate business challenges into practical technology solutions.
- Experience leading adoption and change management initiatives.
- Ability to manage multiple priorities and work independently in a dynamic environment.
- Passion for innovation, digital transformation, and continuous improvement.
Education and Experience
- Bachelor's degree in Business, Information Systems, Engineering, Computer Science, Technology, or a related field. Equivalent combinations of education and experience will be considered.
- 5+ years of experience delivering Microsoft 365, Power Platform, digital transformation, automation, or technology-enabled business solutions.
- Experience building or supporting AI-enabled workflows, Copilot solutions, automations, or digital transformation initiatives.
- Experience working with stakeholders across multiple business groups and levels of an organization.
- Experience within the Architecture, Engineering, and Construction (AEC) industry is considered an asset.
- Microsoft, Power Platform, AI, Copilot, or related certifications are considered an asset.
The preceding job description is not intended to describe in detail the multitude of tasks that may be assigned, but rather to provide a general overview of the expectations and responsibilities of this position. As the nature of business demands change, so may the functions of this position. Additional duties and responsibilities may be assigned.
Typical office environment working with computers and remaining sedentary for long periods of time. Field work may include exposure to the elements including inclement weather.
Stantec is a place where the best and brightest come to build on each other's talents, do exciting work, and make an impact on the world around us. Join us and redefine your personal best. #DesignYourPlace!
Pay Range:
- Locations in CO, HI, IL, MD & Various CA, CT, NJ, VA Areas-$96,100.00 - $139,400.00 Annually
- Locations in WA, DC & Various CA, MA, VA Areas-$103,100.00 - $149,500.00 Annually
Primary Location: United States | CO | Denver
Other Locations: United States | CO | Austin
Organization: 2361 Buildings-US Assets-Denver CO
Employee Status: Regular
Management Level: Individual contributor
Business Justification: New Position
Travel: No
Schedule: Full time
Job Posting: 20/10/2026 01:10:11
Req ID: 1008332
Stantec provides equal employment opportunities to all qualified employees and applicants for future and current employment and prohibit discrimination on the grounds of race, colour, religion, sex, national origin, age, marital status, genetic information, disability, sexual orientation, gender identity or gender expression. We prohibit discrimination in decisions concerning recruitment, hiring, referral, promotion, compensation, fringe benefits, job training, terminations or any other condition of employment. Stantec is in compliance with laws and regulations and ensures equitable opportunities in all aspects of employment. At Stantec we are committed to ensuring our recruitment process is accessible to all. If you require reasonable adjustments to be made during the recruitment process then please inform a member of our Talent Acquisition team.
Salary Context
This $96K-$149K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Stantec, 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 $212,040 based on 13,780 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($122K) sits 42% below the category median. Disclosed range: $96K to $149K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Stantec AI Hiring
Stantec has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Denver, CO, US. Compensation range: $149K - $149K.
Location Context
AI roles in Denver pay a median of $200,000 across 143 tracked positions. That's 6% below the national 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.