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Role: AI Enablement Consultant Location: St Louis, MO(Hybrid) Duration: 12 Months Contract(Extension up to 24 months is possible) Interview Mode: Onsite at Client HQ. Will likely be one round with a panel. A 2nd round may be possible if the manager is trying to make a decision between two candidates.
Purpose of the roleThe hiring manager runs the AI Center of Excellence, which focuses on AI governance and enablement across the organization. This hire would be the connective thread between the CoE and the business lines, helping people understand what AI tools they can use and how to use them well.
Main responsibilities AI Champions program: Primary focus. The first cohort of about 15 people is kicking off soon, and this person would join right after. They would liaise with and manage the cohort, build structure around program expectations, and define how outcomes are measured. Enablement and support: Act as a go-to resource for general questions on available AI capabilities, and lead hands-on sessions and problem-solving, especially for Copilot. Materials creation: Talk with people about their problems, then turn those conversations into reference files and guides that support upskilling and AI literacy. Dialogue building: Encourage open, collaborative conversation about how AI is used across the business.
Must-have skills
- Copilot knowledge: Working familiarity with Copilot and its capabilities, plus general AI tool literacy (the one clear hard skill).
- Communication: The top priority. The ability to distill complex information into clear, usable content. The manager acknowledges this is hard to list as a resume skill, and that the team already has plenty of technical strength but needs more of this.
- Organization / light project management: Enough structure to run a cohort program and keep materials and follow-ups on track, without needing a full project manager.Arrangements
- Will be hybrid 3 days a week in-office. One day will need to be a Wednesday and the one day will need to be either a Monday or Friday.Extend or Convert:
- Extension up to 24 months is possible. The manager doesn't see this position or work going away.
- Conversion depends on need, headcount, and budgetInterview Process:
- Onsite at Client HQ. Will likely be one round with a panel. A 2nd round may be possible if the manager is trying to make a decision between two candidates.
Position SummaryThe AI Enablement Consultant plays a key role in accelerating AI adoption and literacy across Client. This individual will be part of the AI Center of Excellence (AI CoE) team, working cross-functionally with business line teams and other partners to support AI efforts and help co-workers understand, adopt, and effectively use approved AI tools and capabilities.This role combines strong communication, organization, and change enablement skills with hands-on knowledge of Microsoft Copilot and related AI technologies. A primary focus area will be supporting and growing the AI Champions program, ensuring Champions have the resources, guidance, and community needed to drive AI adoption within their department.
Key Responsibilities AI Literacy & Enablement· Support the development and execution of AI literacy and learning initiatives.· Create and maintain AI learning materials, job aids, communications, and adoption resources.· Help co-workers understand approved AI tools, responsible use practices, and opportunities to apply AI in their daily work.· Identify adoption barriers and recommend strategies to improve engagement and utilization.
AI Champions Program· Help manage and coordinate the AI Champions network.· Partner with Champions to share best practices, success stories, and lessons learned.· Facilitate Champion meetings, communications, events, and engagement activities.· Develop toolkits, resources, and support materials that enable Champions to educate and assist their local teams.· Monitor program effectiveness and gather feedback to continuously improve the Champion experience.Program Coordination & Communications· Coordinate multiple enablement initiatives simultaneously while managing competing priorities.· Track program activities, milestones, communications, and stakeholder engagement.· Develop executive-ready presentations, status updates, and communications materials.· Support planning and execution of AI learning sessions.
Ideal Candidate Required Skills & Attributes · Exceptional written, verbal, and presentation communication skills. · Highly organized with strong attention to detail and follow-through. · Self-starter who can work independently with limited direction. · Strong relationship-building and stakeholder management skills. · Ability to translate technical concepts into clear, business-friendly language. · Strong facilitation and group engagement skills. · Comfortable working in a fast-paced and evolving environment. · Demonstrated experience using Microsoft Copilot and other generative AI tools. · Experience developing communication materials, presentations, and learning content.
Success Measures Success in this role will be demonstrated through: · Increased adoption and effective use of approved AI tools. · Growth and engagement of the AI Champions network. · Positive business stakeholder feedback. · Delivery of high-quality enablement materials and learning experiences. · Strong partnerships between the AI CoE and business line teams. · Increased awareness and understanding of AI capabilities
Pay: $55.00 - $60.00 per hour
Work Location: Hybrid remote in St. Louis, MO 63103
Salary Context
This $114K-$124K 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 Noise Consulting Group, 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 ($119K) sits 44% below the category median. Disclosed range: $114K to $124K.
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.
Noise Consulting Group AI Hiring
Noise Consulting Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in St. Louis, MO, US. Compensation range: $124K - $124K.
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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