Interested in this AI/ML Engineer role at Wight & Company?
Apply Now →Skills & Technologies
About This Role
Are you looking for an opportunity to do challenging, creative work that makes a real impact? Would you like to be part of a company that respects individual contributions and thrives on collaboration? Do you love to win? Then consider joining Wight & Company. We're a nationally recognized, award-winning architecture, engineering, and construction firm known for a dynamic company culture that fosters innovation and challenges the status quo, and we're looking for a talented professional to join our team.
Wight & Company is seeking an AI Solutions Specialist to help our teams apply AI to the work of designing and constructing buildings. You will work directly with architects, engineers, estimators, construction professionals, and business teams to understand their needs, build useful tools, and help colleagues use AI confidently and effectively.
Your first six months will focus on helping teams use Microsoft Copilot more effectively and putting several agents into regular use for specific team needs. You will have access to Copilot's agent-building tools, Copilot Studio, Power Automate, and a testing environment.
We welcome candidates from AI, data, software, BIM, architecture, or construction management. You will work with our Chief Innovation Officer, IT team, digital practice colleagues, and technical partners as needed.
What you will do
- Learn how teams across the firm work, then help the Chief Innovation Officer select a manageable set of priorities. Turn those priorities into tools with clear user needs and success criteria.
- Build and improve agents with Copilot's agent-building tools, Copilot Studio, and Power Automate. Help establish Copilot Studio and testing practices with IT support.
- Lead demonstrations, small-group training, and individual coaching. Create examples and simple guides that help colleagues develop their own skills.
- Work with users to define useful outputs, test accuracy against trusted information, and establish when professional review is needed.
- Identify useful data and document its sources, ownership, definitions, and relationships across systems.
- Use Python, SaaS APIs, and approved tools to connect data and automate workflows. Work with IT and technical partners on more complex integrations.
- Help teams identify important information in unstructured documents and records, and use AI tools to extract, standardize, and validate it.
- Apply firm guidelines for confidentiality, permissions, data security, and responsible AI use. Involve the appropriate specialists in decisions that require additional review.
- Own and maintain the agents and tools you launch. Provide ongoing support, resolve issues, update documentation, and improve solutions as team needs and source systems change. Track usage and results.
- Work with designers, engineers, builders, estimators, and BIM teams to develop consistent data workflows that can feed AI tools to enhance processes such as building programming, estimating, risk management, scheduling, sustainability, and material and detail performance.
What you bring
- An approachable communication style, strong listening skills, and patience when helping people learn unfamiliar tools.
- Hands-on experience with generative AI and evidence of building an agent, automation, or other useful tool. Coursework, internships, and personal projects count.
- Practical Python skills are required. Write and modify scripts with AI assistance as appropriate, while independently reading, understanding, explaining, testing, and troubleshooting the code and validating its results.
- Practical problem-solving skills, including breaking an unclear request into steps, testing assumptions, and troubleshooting when an approach fails.
- Basic data literacy: comfort working with tables, recognizing missing or inconsistent information, and understanding how records relate across systems.
- Curiosity about architecture, engineering, construction, and BIM, with a willingness to learn from the people doing the work.
- Sound judgment about confidential information, access permissions, AI limitations, and when to ask for help.
- The ability to work in person in our Darien, Illinois office.
Helpful experience
Experience with one or more of the following would be helpful:
- Microsoft Copilot, Copilot Studio, Power Automate, SharePoint, or comparable agent and automation tools.
- Data management, data integration, or data engineering experience. Experience helping design or implement a data lake or other shared data platform is particularly valuable.
- JavaScript, SQL, SaaS APIs, or other tools for connecting and working with information.
- Revit, Autodesk Construction Cloud, estimating applications, construction platforms, or other project information systems.
- Facilitating training, documenting workflows, or helping a team adopt a new tool.
- We consider relevant education, practical experience, self-directed learning, and project portfolios. A specific degree or career path is not required.
The opportunity
- Working in person in Darien puts you alongside the people who design, estimate, and build projects. We can support candidates who bring AEC knowledge and are developing AI skills, or AI and data experience with an interest in learning AEC.
Salary Range: $80,000 - $125,000. Individual pay will be based on several factors including experience, knowledge, skills, and abilities of the applicant.
Wight & Company offers excellent benefits and a wonderful work environment. Our comprehensive benefits including medical, dental, vision, disability, life insurance, flexible spending, paid holidays, paid time off, and a 401(k). Our flexible work schedule allows for a healthy work-life balance. For more information about our company, visit our website at www.wightco.com.
Wight & Company is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetics, protected veteran status, disability status, or any other characteristic protected by federal, state, or local laws.
Wight & Company values diversity in our workforce. We are committed to recruiting, hiring, and promoting people with disabilities and veterans. If you need an accommodation to assist with completing the electronic application, please call us at 630.969.7000 and ask to speak with a Human Resources representative.
#LI-NS1
Salary Context
This $80K-$125K 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 Wight & Company, 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 ($102K) sits 52% below the category median. Disclosed range: $80K to $125K.
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.
Wight & Company AI Hiring
Wight & Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Darien, IL, US. Compensation range: $125K - $125K.
Location Context
Across all AI roles, 6% (708 positions) offer remote work, while 10,737 require on-site attendance. Top AI hiring metros: New York (3,257 roles, $221,000 median); San Francisco (2,766 roles, $260,550 median); Los Angeles (1,580 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 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.