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AI Enablement Specialist
Position Title: AI Enablement Specialist
Department: Sales
FLSA Status: Non-Exempt
Location: Hybrid (In office 3 days per week)
Reports to: Chief Sales Officer
Effective Date: October 2026
Choose Chicago is the official sales and marketing organization for our city, promoting Chicago as a global visitor destination to business and leisure travelers.
POSITION SUMMARY
Choose Chicago is seeking a highly motivated, innovative, and adaptable professional to serve as the organization’s AI Enablement Specialist. This newly created role represents an exciting opportunity to help shape the future of one of the nation’s leading destination marketing organizations as artificial intelligence and emerging technologies become increasingly integrated into daily business operations and long-term strategy.
This role is approximately 70% dedicated to serving as the organization's primary internal liaison to Choose Chicago's external AI consultancy, coordinating implementation of system automation and technology initiatives and helping build a culture that fully embraces the adoption of AI and innovation across the organization including Sales, Marketing, Communications, Client Services, Human Resources, Finance, and Operations.
The remaining 30% provides executive-level administrative support to the Chief Sales Officer, Vice Presidents of Sales, and Vice President of Client Services (collectively, “Sales and Client Services leadership”); these Sales Executives will rely on the Specialist to help manage communications and relationships with Choose Chicago leadership, clients, partners, and staff.
The ideal candidate will be experienced in handling a wide range of project management and executive support related tasks and must have a high level of interpersonal skills to handle sensitive and confidential situations. The candidate must be exceedingly well organized, flexible, and able to work in a fast-paced environment while maintaining an overall awareness of the Sales Executives’ business activities. As the organization matures in its AI adoption, the Specialist will also look for opportunities to streamline these support functions using AI and automation tools where appropriate, reflecting the role's broader charge to model AI adoption within the department. Reporting directly to the Chief Sales Officer, this role offers regular exposure to executive leadership and department heads during a period of organizational modernization.
ESSENTIAL DUTIES AND RESPONSIBILITIES
AI Enablement and Implementation (approximately 70%)
- Serve as Choose Chicago's primary internal point of contact for the organization's external AI and technology consultancy.
- Coordinate communication, implementation timelines, project follow-up, and cross-departmental collaboration related to AI and automation initiatives.
- Support the rollout of customized AI solutions and technology tools, working with departments to identify opportunities for operational improvement, automation, and workflow efficiency.
- Assist in developing and documentation of processes and best practices for AI implementation and technology adoption.
- Help build and foster a culture that fully embraces the adoption of AI and innovation across the organization.
- Monitor emerging AI and technology trends relevant to destination marketing, hospitality, and tourism operations.
- Support staff training and onboarding on new technology systems and AI tools and develop internal communications and adoption resources.
- Track implementation progress, usage metrics, and staff feedback, and report status to the Chief Sales Officer.
- Comply with established policies and guidelines governing data privacy, confidentiality, and Choose Chicago operations in all AI and automation work.
Sales & Client Services Leadership Support (approximately 30%)
- Drafts, edits, and formats letters, memos, presentations, and other documents on behalf of Sales and Client Services leadership.
- Assists with the creation of presentations.
- Manages Sales and Client Services leadership's fast-paced schedules by planning and scheduling meetings, conference calls, and calendar logistics.
- Assists leadership in the timely and seamless management of all communications, written and via telephone, email, and voicemail, with a variety of constituents.
- Prepares leadership for weekly calendars of activities, including prioritization of meetings and inclusion of documents and meeting materials.
- Reviews calendars and outstanding projects weekly with Sales and Client Services leadership to ensure all due preparation is completed.
- Prepares agendas, as needed, for meetings with high-level clientele and partners.
- Creates and distributes meeting notices and invitations on behalf of Sales and Client Services leadership.
- Communicates professionally on behalf of Sales and Client Services leadership with staff, Board members, clients, and stakeholders.
- On own initiative or as assigned, assists with and participates in projects, functions, and special events.
- Performs other duties as assigned.
MINIMUM QUALIFICATIONS
- Bachelor's degree in Technology, Information Systems, or Business or equivalent experience required; a background in Hospitality Management, Marketing, or Communications is a plus.
- Prior experience providing executive-level administrative support to senior leadership preferred.
- Strong interest and foundational knowledge in artificial intelligence, emerging technologies, and digital transformation.
- Proven project management experience with high attention to detail managing multiple, sometimes competing, priorities in a fast-paced environment.
KNOWLEDGE, SKILLS, AND ABILITIES
- Proficient in using workplace AI technologies and automation tools to improve efficiency while maintaining strict adherence to data privacy, confidentiality, and company policies.
- Ability to effectively communicate both verbally and in writing with a strong knowledge of spelling, grammar, and punctuation.
- Ability to represent Choose Chicago in a professional manner while interfacing with internal and external stakeholders; excellent telephone and interpersonal skills.
- Ability to communicate technical concepts in approachable and practical terms to non-technical staff.
- Superior organizational skills and attention to detail; ability to multi-task and complete multiple projects and requests simultaneously and accurately.
- Must be able to maintain the highest level of confidentiality and discretion when handling sensitive information.
- Exceedingly well organized, flexible, and able to work in a fast-paced environment with limited supervision.
- Proficiency in Microsoft Office Suite, including Word, Excel, PowerPoint, and Outlook, and comfort learning new technology platforms quickly.
PHYSICAL REQUIREMENTS AND ENVIRONMENTAL CONDITIONS
- Ability to perform computer work for extended periods of time.
- Ability to sit for extended periods of time in performing the work.
- Ability to travel by ground transportation as required in performing the work.
- Ability to work in-office a minimum of three (3) days per week.
- Occasional evening and event work required.
Salary Context
This $65K-$75K 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 Choose Chicago, 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 in Demand for This Role
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 ($70K) sits 67% below the category median. Disclosed range: $65K to $75K.
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.
Choose Chicago AI Hiring
Choose Chicago has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US. Compensation range: $75K - $75K.
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
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