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About This Role
Description: AI Solutions Lead*What we are:* Fun, fast\-paced, client\-focused, people\-centric, inviting, flexible, and rapidly growing.*What we are not:* Overly structured, uncompromising, disengaged, or disconnected.
We are looking for a driven, curious, and business\-minded AI Solutions Lead to become the first dedicated AI expert on our Information Technology team. This is a unique opportunity to help shape how AI is adopted across our organization—from identifying high\-impact use cases and leading cross\-functional initiatives to establishing governance, evaluating emerging technologies, and empowering teams to work smarter. The ideal candidate thrives in the space between a project leader and a technical builder: someone who can manage AI initiatives, translate business needs into practical solutions, answer questions from across the organization, and isn't afraid to roll up their sleeves to solve technical challenges when needed. If you're energized by innovation, enjoy bringing structure to emerging technologies, and want to make a measurable impact across an entire organization, we'd love to meet you. The work we do is categorically awesome, and we're looking for someone with the experience, curiosity, and collaborative mindset to help our business put AI to work.
Company DescriptionWe are a leading provider of wireless, mesh\-networked, “Internet of Things” (IoT) sensors for industrial environments. Our cost\-effective, plug\-and\-play products empower plants of all sizes to conduct predictive and preventative maintenance on mission\-critical and secondary equipment in order to maximize uptime. (I.e. We help with things like getting your Prime packages to you faster!) Reports to: Information Technology Manager
Location: Cincinnati, OH
Onsite Requirements: Onsite, remote flexibility as needed
Travel: No travel required
Work Authorization: Must be authorized to work in the U.S.
\*\*Sponsorship is not available at this time\*\*
Exemption Status: Exempt
Job Description
The AI Solutions Lead will serve as the central point of contact and internal translator between the business and emerging AI technologies. This role owns the portfolio of internal AI initiatives end\-to\-end: intake, prioritization, project management, governance, emerging\-technology evaluation, and hands\-on solutioning. The ideal candidate is a business liaison who can turn vague “can AI do this?” questions into scoped, practical use cases — and who has enough technical fluency to guide a project built with tools such as Claude or Replit at the beginning, middle, or end, and to recognize when a development direction doesn’t make sense. They will report to the IT Manager, operating with broad autonomy to make day\-to\-day decisions independently while collaborating on new vendors, meaningful spend, and policy.
This position will give you the opportunity to support:
AI Project Management:
- Own intake, prioritization, and the roadmap for internal AI initiatives.
- Maintain visibility across the business to prevent uncoordinated “shadow” AI efforts.
- Proactively seek opportunities within the business for improvements in process.
- Prioritization of specialized projects needing additional expertise.
- Develop norms for communicating project status, issues, and risks to leadership.
- Be a champion for in\-flight projects to ensure completion within project timeframes.
AI Governance \& Policy:
- Maintain and evolve responsible\-use standards and data\-handling rules for AI tools.
- Operate a tool and vendor review process that keeps sensitive data protected.
- Ensure AI practices align with relevant security and compliance requirements (e.g., SOC II, ISO\-9001\).
Emerging Technology \& Evaluation:
- Stay informed on the AI landscape and industry trends; horizon\-scan for relevant capabilities.
- Understand the use of GitHub and basic development principles.
- Run small proofs\-of\-concept and pilots to validate value before broad adoption.
- Make pragmatic build / buy / wait recommendations rather than chasing every release.
Business Enablement \& Support:
- Serve as the first point of contact for AI questions from across the business.
- Translate ambiguous requests into well\-scoped, achievable use cases.
- Train and upskill staff to adopt approved AI tools effectively.
- Intake of completed AI applications for continued maintenance.
Hands\-On Solutioning:
- Build lightweight automations, prompt\-based tools, and prototypes to prove value quickly
- Get colleagues unstuck on AI\-assisted development projects (e.g., Claude, Replit) at any stage.
- Read across common programming languages well enough to judge whether an AI\-generated development direction is sound — without needing to write production code from scratch.
- Make independent day\-to\-day decisions, escalating new vendors, meaningful spend, and policy changes to the IT Manager.
- Carry out additional responsibilities as needed to support business objectives
Requirements:
Qualifications \& Requirements:
- Bachelor’s degree in Computer Science, Information Technology, or related field, or equivalent practical experience
- Have 2 \- 3 years of experience in IT, technology, or a related field
- Strong technical judgment: able to read and reason about code across common programming languages and assess solution quality, without needing to build production software from scratch
- Hands\-on familiarity with modern AI tools (e.g., LLM assistants such as Claude, low\-code/build platforms such as Replit), prompt engineering, and APIs
- Excellent communication and interpersonal skills; able to translate between business needs and technical solutions
- Demonstrated ability to turn ambiguous business needs into practical, scoped solutions; strong problem\-solving skills
- Project management skills and the ability to operate independently with broad latitude
- Familiarity with AI governance, data privacy, and risk considerations
- Familiarity with legal, statutory/regulatory requirements such as: ISO\-9001, GDPR and SOC II desired
- Familiarity with the Google G\-suite desired
Culture Fit Requirements
- High intellect and strong analytical skills
- Innovative and driven to constantly improve
- Ability to balance strategic vision with operational execution
- Non\-bureaucratic, flexible, and able to thrive in a fast\-moving environment
- Positive energy and attitude towards people and problem\-solving
- Demonstrates a balance of humility, strong work ethic, and interpersonal savvy (“Humble, Hungry, and Smart”) in approach to teamwork and problem\-solving
- “Can Do” mentality
*Why Waites?*
At Waites, we offer an attractive base salary with a strong history of profit sharing. We are intensely customer\-focused, with a culture that encourages hard work, fun, and mutual success. As we continue to grow, there will be many opportunities for professional advancement and development. If you are a talented AI professional looking for an exciting new challenge, please apply!
Learn more about who we are and what we do by checking out our website at www.waites.net.
WST is an Equal Opportunity Employer.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Waites sensor technologies, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Waites sensor technologies AI Hiring
Waites sensor technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cincinnati, OH, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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