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About This Role
The AI & Automation Engineer is a hands-on technical role responsible for designing, building, deploying, and supporting AI agents, retrieval-augmented generation (RAG) capabilities, and automation solutions on Company-approved AI platforms. The position transforms prioritized use cases into secure, supportable production solutions, develops reusable patterns and knowledge layers, and serves as the primary technical builder for the Company's AI and automation programs under the direction of the Senior Manager of Infrastructure and AI. This position will require regular (every week) in-person attendance at one of our Foley Equipment offices with a partial week work-from-home opportunity. At this time, fully-remote is not available. Kansas City or Park City preferred. What you will do:Solution Design and Development* Design, build, test, and deploy AI agents in Microsoft Copilot Studio and Foundry (topics, actions, tools, connected agents, knowledge sources) and publish them to approved channels such as Microsoft Teams.
- Build the Power Platform components behind AI use cases: Power Automate flows, Dataverse data models and security roles, model-driven and canvas apps, AI Builder prompts, and Adaptive Card review experiences in Teams.
- Integrate agents and flows with enterprise systems (ERP, CRM, data platform, Microsoft 365) using connectors, REST APIs, Microsoft Graph, Azure Functions, Logic Apps, and Model Context Protocol (MCP) tool connections.
- Move prototypes into production through environment strategy, solution packaging and application lifecycle management (ALM), publishing, and release validation. Assist with evaluating the cost of proposed solutions.
- Write supporting code (Python, PowerShell, C#, or JSON) where low-code tooling falls short and use AI-assisted development tools to accelerate delivery and legacy code modernization.
AI Platform and Knowledge Engineering* Deploy and manage Azure AI Foundry projects, model deployments, and agents using Company-owned Azure Storage, Azure AI Search, and Cosmos DB so source data and vector indexes remain within the Company's tenant.
- Build retrieval-augmented generation (RAG) pipelines (ingestion, chunking, embedding, indexing, vector and hybrid search) as reusable knowledge and context layers shared by multiple agents and automations.
- Configure network isolation and identity for AI workloads, including private endpoints, private DNS, managed identities, and role-based access control (RBAC), and troubleshoot connectivity and authorization failures across platform boundaries.
- Write system prompts and agent instructions, implements tool calling and orchestration, and run evaluations (test sets, groundedness and accuracy checks, regression tests) before and after each release.
Security, Governance, and Responsible AI* Build every solution in accordance with Company security and data protection requirements, including least-privilege identities, scoped security roles, Entra ID app registrations, Power Platform data loss prevention (DLP) policies, sensitivity label requirements, and vendor and customer confidentiality obligations.
- Verify each agent’s authentication model, including end-user, maker, and agent-author credentials, to ensure the agent returns only information the requesting user is authorized to access. Apply content-safety controls, input and output validation, and human review for consequential actions.
- Support the AI intake and review process with technical assessments, help identify and remediate unsanctioned AI usage, and support Microsoft 365 Copilot readiness, including permissions and oversharing remediation.
Operations, Support, and Enablement* Monitor delivered solutions for accuracy, performance, usage, security, cost, and continued fit with the business need; track licensing, token, credit, and capacity consumption and recommended actions to control spend.
- Provide third-tier support and root-cause analysis for production AI and automation solutions, including occasional after-hours work for urgent issues or production automation failures.
- Maintain documentation for each solution (business owner, purpose, architecture, data sources, access model, prompts, testing notes, support owner, change history, retirement criteria) and follow Company change, release, and lifecycle practices.
- Deliver demos and technical training on approved AI tools, mentor citizen developers and IT colleagues on approved patterns and review their work for security and supportability before production.
- Communicate the status, risks, dependencies, and expected business value of assigned use cases to the Senior Manager of Infrastructure and AI and business stakeholders.
Our ideal candidate:* Strong understanding of generative AI, AI agents, retrieval-augmented generation, automation, low-code/no-code development, and practical business process improvement.
- Hands-on experience with Microsoft’s AI tech stack including Copilot Studio, Azure AI Foundry, Azure OpenAI, Azure AI Search, Power Platform (Power Automate, Power Apps, Dataverse, AI Builder), Fabric, and Microsoft 365 Copilot.
- Working knowledge of Azure networking, Entra ID, managed identities, and role-based access control as they apply to AI workloads.
- Proficiency in at least one language (Python, PowerShell, C#, or TypeScript), REST APIs, JSON, and Git.
- Strong knowledge of data privacy, security, access controls, responsible AI practices, and change management.
- Ability to diagnose problems across platform boundaries using logs, run histories, and vendor documentation, and to explain technical designs in plain language to leaders at all levels.
- Must be well-organized, practical, collaborative, and pay close attention to detail.
- Ability to drive and valid driver’s license required.
Education and Experience* Bachelor’s degree in computer science, information systems, data analytics, or a related field is required.
- Three years of proven experience in application development, automation, data, or related information technology work, including one year of experience building and deploying production generative AI solutions such as agents, RAG solutions, or LLM-based automations.
- Hands-on experience with Microsoft Copilot Studio, Power Platform, and Azure AI Foundry required.
- Experience with Azure AI Search, Microsoft Fabric, and ERP or CRM integration (Dynamics 365 or similar) preferred.
- Microsoft AI-102, PL-400, or AZ-204 certification preferred.
Physical Demands (The physical demands described here must be met by an employee to perform the essential functions of this job successfully. Reasonable accommodations may enable individuals with disabilities to perform essential functions.) Regularly required to sit, see, speak and hear —frequent and regular use of hands and fingers to handle or feel. Recurring close vision, distance vision, color vision, peripheral vision, depth perception, and ability to adjust focus. Constantly use hands and fingers to perform computer keyboard work. The ability to drive, and availability and capability to travel for business purposes. Work Environment (The work environment characteristics described here represent those an employee encounters while performing the essential functions of this job. Reasonable accommodations may enable individuals with disabilities to perform essential functions.) Perform most of the work in a relaxed environment but may be exposed to outside weather conditions. The noise level in the work environment is usually moderate. Encounter different temperatures while driving and getting in and out of a vehicle. Encounter different climates when traveling. What We Can Offer You:* Competitive pay to recognize your valuable contribution
- 401(k) with matching
- Comprehensive medical, dental, and vision plans
- Basic Life & AD&D, short-term disability, & and long-term disability
- Wellness programs to focus on building healthy habits while earning prizes and cash bonuses throughout the year
- Holiday, PTO, and Parental Leave, promoting work-life balance and personal growth
- Casual dress attire
- Family-owned company where you’re more than just another employee
Foley Industries – Foley Equipment, Foley Power Solutions, Foley Rental, and SITECH Central – proudly stands at the forefront of progress, serving customers in Kansas and Western Missouri for over nine decades. With a dedicated team of over 1,000 employees, we are committed to delivering exceptional service, top-quality parts and rentals, and unparalleled sales support across a comprehensive range of Caterpillar and allied construction equipment, power generation solutions, and cutting-edge SITECH construction technology. As we continue to empower our customers' success, we invite you to explore the endless opportunities that await you at Foley. To learn more about our company and join our team of industry leaders, please visit www.FoleyEq.com.* At this time Foley does not participate in any Visa sponsorship programs.
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 Foley Equipment 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.
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.
Foley Equipment Company AI Hiring
Foley Equipment Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Kansas City, MO, US.
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
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