Interested in this AI/ML Engineer role at CRAWFORD, MURPHY & TILLY?
Apply Now →Skills & Technologies
About This Role
Crawford, Murphy & Tilly is seeking an AI Solutions Lead to join the Corporate Services team in the Chicagoland area.This is not a remote position.
The AI Solutions Lead is responsible for identifying, shaping, delivering, and scaling practical artificial intelligence and automation solutions that improve CMT’s project delivery, business operations, knowledge access, and employee experience. The role serves as a bridge between engineers, project managers, business leaders, Technology, Data Analytics, and external solution providers, translating operational needs into secure, usable, and measurable AI-enabled capabilities.
This position combines AI product leadership, business process improvement, solution design, implementation oversight, governance support, and change management. The AI Solutions Lead will build a prioritized portfolio of use cases, guide solutions from discovery through adoption, and help establish repeatable methods for responsible AI at CMT. The role is expected to remain close to CMT’s engineering and consulting workflows so that solutions address real business needs and can be sustained after launch.
We are a fast-growing firm with 500+ employees across 25 offices in 10 states serving clients through our four primary business groups:* Aviation
- Surface Transportation
- Water Resources
- Building & Site Services
The AI Solutions Lead is an experienced professional focused on applying expert-level knowledge and skills to assigned projects.
Supervisory Responsibilities
- Formal direct supervisory responsibilities may be assigned as the function grows.
- Lead cross-functional solution teams through influence, clear product direction, and coordinated delivery.
- Provide dotted-line guidance to AI champions, citizen developers, subject matter experts, interns, consultants, and vendors supporting AI initiatives.
- Coach employees in responsible AI use, solution design, testing, adoption, and continuous improvement. What you will do:
- Partner with business units and corporate functions to discover, document, and prioritize AI and automation opportunities tied to defined business needs.
- Map current-state workflows, identify bottlenecks and manual handoffs, and design improved future-state processes enabled by AI, automation, data, or digital tools.
- Build and maintain an enterprise AI solutions roadmap, including use-case value, feasibility, risk, dependencies, sequencing, ownership, and adoption requirements.
- Translate ambiguous business needs into clear product requirements, user stories, acceptance criteria, process designs, and implementation plans.
- Lead rapid prototypes and pilots using approved platforms, including Microsoft 365 Copilot, Copilot Studio, Power Platform, Azure AI services, and other CMT-approved technologies.
- Coordinate solution development with Technology, Data Analytics, Dev, cybersecurity, legal, risk, records management, business leaders, vendors, and end users.
- Support CMT’s responsible AI governance practices, including intake, risk review, data handling, human oversight, documentation, testing, release, and ongoing monitoring.
- Create implementation, training, communication, and support plans that help employees confidently adopt new solutions and use them appropriately.
- Develop reusable solution patterns, templates, playbooks, and standards to accelerate future AI and automation work across CMT.
- Identify opportunities to improve engineering, planning, architecture, environmental, and project management workflows, including project setup, scoping, design production, quality processes, knowledge management, reporting, and closeout.
- Support business-operation improvements in areas such as finance, human resources, marketing, business development, and corporate administration.
- Facilitate demonstrations, workshops, office hours, and communities of practice that build practical AI literacy and surface new use cases.
- Support external thought leadership where appropriate, including conference presentations, client conversations, recruiting, and industry partnerships.
Here is what you’ll need:
- Bachelor’s degree in engineering, computer science, information systems, data science, business, product management, or a related field. An equivalent combination of education and relevant experience may be considered.
- Graduate education in business, artificial intelligence, analytics, engineering, or a related discipline is preferred.
- Progressive experience leading technology-enabled business improvement, AI products, automation, digital transformation, engineering technology, or related initiatives.
- Professional Engineer licensure, product management credentials, Microsoft certifications, change-management credentials, or comparable qualifications are valued but not required.
- Ability to connect business strategy, engineering workflows, user needs, data, and technology into practical solutions.
- Working knowledge of generative AI, large language models, machine learning concepts, workflow automation, data integration, and AI product development.
- Ability to translate between technical and nontechnical audiences and communicate tradeoffs, constraints, risks, and recommendations clearly.
- Strong product/project management skills, including discovery, prioritization, roadmapping, requirements, experimentation, launch, adoption, and lifecycle management.
- Strong business process analysis and improvement skills, including workflow mapping and identification of root causes, bottlenecks, controls, and handoffs.
- Understanding of responsible AI principles, data governance, privacy, cybersecurity, model evaluation, human oversight, and enterprise risk management.
- Ability to develop prototypes or guide technical teams using tools such as Microsoft 365 Copilot, Copilot Studio, Power Automate, Power Apps, Azure AI services, Python, SQL, APIs, or comparable technologies.
- Familiarity with architecture, engineering, or consulting-firm project delivery, including scope, schedule, budget, quality, staffing, and client commitments.
- Advanced analytical, critical-thinking, facilitation, and problem-solving skills.
- Ability to accept occasional out-of-town travel assignments and work outside normal hours when needed to support launches, training, or critical business needs. Measures of Success
- A visible, prioritized portfolio of AI opportunities aligned with CMT business goals and supported by accountable business owners.
- AI solutions that are adopted, supported, governed, and demonstrably improve time, quality, risk, employee experience, or financial performance.
- Repeatable standards and delivery methods that allow CMT to move from isolated experiments to sustainable enterprise capabilities.
- Improved AI literacy and confidence among employees, with clear expectations for responsible use.
- Strong collaboration among operations, business units, Technology, Data Analytics, and other enabling functions. Physical Requirements:
Physical demands vary depending on the specific nature of assigned projects. CMT is committed to providing reasonable accommodations for individuals with disabilities.
- Office tasks: Physical requirements are minimal and include prolonged periods sitting or standing at a desk and working on a computer.
- Travel and field exposure: Occasional travel to CMT offices, client locations, conferences, or project sites may involve extended sitting, walking, climbing, or navigating uneven terrain and active work areas.
- Ability to participate in meetings, workshops, demonstrations, and training sessions conducted in person or virtually. Elevating infrastructure, together.
Crawford, Murphy & Tilly (CMT) is a civil infrastructure professional services firm working with clients and their communities to shape the future, together. Our mission is to provide superior infrastructure solutions, responsive to clients’ needs and expectations, while fostering growth and development for employees. Our dedicated team of engineers, planners, scientists, and consultants work together with clients and their communities to plan, advance, deliver, and manage civil infrastructure.
At CMT, we are looking for individuals who come to us with varied perspectives, backgrounds and worldviews.
Benefits *at*CMT
CMT offers a generous benefits program, focused on four pillars: Health & Wellness, Work + Life Balance, Financial Heath & Retirement, and Career Development. To learn more about the CMT benefit programs, review the information on our website, Careers - CMT Infrastructure Professionals.
The expected compensation range for this position is $100,000-150,000 annually. The final agreed-upon compensation is based on numerous factors, including but not limited to individual education, qualifications, prior work experience, and geographic location.
EQUAL OPPORTUNITY EMPLOYMENT (EEO) POLICY
Crawford, Murphy & Tilly (CMT) is an Equal Opportunity Employer and is committed to providing employment opportunities to minorities, females, veterans and disabled individuals, as well as other protected groups.
ACCOMMODATIONS FOR APPLICANTS
Applicants wishing to apply for an open position with CMT who require a reasonable accommodation in order to complete the application process may contact CMT’s Human Resources Department at [email protected] to request assistance. CMT is an Equal Opportunity Employer.
UNSOLICITED APPLICATIONS
CMT does not accept unsolicited resumes or applications. Staffing and recruiting agencies, and individuals being represented by an agency, are not authorized to use this site or to submit applications or resumes, and any such submissions will be considered unsolicited. CMT is not responsible for any fees related to unsolicited resumes/applications.
Salary Context
This $100K-$150K 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 CRAWFORD, MURPHY & TILLY, 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. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($125K) sits 41% below the category median. Disclosed range: $100K to $150K.
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.
CRAWFORD, MURPHY & TILLY AI Hiring
CRAWFORD, MURPHY & TILLY has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $150K - $150K.
Location Context
AI roles in Chicago pay a median of $193,440 across 429 tracked positions. That's 9% 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 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.