Interested in this AI/ML Engineer role at Wheeler Machinery Co?
Apply Now →Skills & Technologies
About This Role
The Director, AI & Data leads the Campbell Companies Center of Excellence (CoE) for data, AI, automation, and software engineering. This role is accountable for the enterprise data platform, data engineering, data governance, AI and agentic AI governance, automation, software development, and data and AI enablement across Campbell Companies and Wheeler Machinery. The Director builds and leads the operating model, organization, and platform that turns trusted data into governed AI and measurable business outcomes. The Director operates as a peer to the Director of Information Technology, collaborating closely on shared infrastructure, security, and end-user delivery while maintaining clear ownership of the data, AI, and automation domain. This is a foundational leadership role, critical to Campbell Companies’ digital transformation strategy.
Duties and Responsibilities
CoE Strategy, Operating Model & Governance:
- Define and execute the AI & Data strategy, multi-year roadmap, and operating model for Campbell Companies, aligned to business priorities and value targets.
- Design and evolve the CoE organizational framework across data engineering, data platform, AI and agentic solutions, automation, software engineering, and data enablement, with clear ownership, intake, prioritization, and service-level expectations.
- Establish and enforce enterprise standards, architecture review, and decision rights so that business-unit and vendor-led work follows CoE guardrails no ungoverned shadow AI, shadow data, or shadow automation.
- Define KPIs and value-realization reporting (adoption, cost, quality, risk, and business outcomes) and present the AI & Data roadmap, governance posture, and results to the VP of Digital quarterly.
Data Architecture & Platform:
- Own the enterprise data platform built on Microsoft Fabric: medallion Lakehouse architecture (Bronze/Silver/Gold), OneLake governance, Fabric Data Factory, Real-Time Intelligence, and Power BI semantic models.
- Set architecture standards for ingestion, transformation, modeling, and serving, including reusable patterns, data contracts, and domain-aligned data products.
- Govern OneLake naming conventions, workspace design, partitioning, shortcuts, and access patterns.
- Manage Fabric capacity, performance, and cost (FinOps), including capacity planning, workload isolation, and chargeback or showback.
- Integrate enterprise source systems (ERP, finance, service, parts, telematics, and equipment data) into a consistent, governed analytical foundation.
Data Engineering & Platform Operations:
- Lead data engineering practices: pipeline development, orchestration, testing, version control, and CI/CD for Fabric and Azure data assets.
- Operate the data platform as a product, with defined SLAs, monitoring, observability, incident response, and root-cause management.
- Embed data quality, lineage, and catalog management into all pipeline operations, with automated validation at each medallion layer.
- Drive platform engineering and infrastructure-as-code standards in partnership with the Director of IT.
Data Governance, Security & Privacy:
- Own the data governance framework: data ownership and stewardship, business glossary, certified datasets, master and reference data, retention, and classification (e.g., Microsoft Purview).
- Implement role-based, row-level, and column-level security and sensitivity labeling in partnership with Information Security.
- Ensure compliance with applicable data privacy and AI regulations, customer and OEM/partner data agreements, and internal audit requirements.
AI Governance & Risk Management:
- Establish and enforce AI governance: use case registry, risk classification, approved tool and model standards, and sunset criteria.
- Align the governance framework to recognized standards (e.g., NIST AI RMF, ISO/IEC 42001) and chair the AI governance or review board.
- Define requirements for model evaluation, testing, bias and safety review, human oversight, auditability, and incident response before and after production release.
- Ensure AI solutions are deployed with the same rigor as any enterprise system, including security review, data-access controls, and change management.
AI Solution Delivery:
- Govern the evaluation, prototyping, and production deployment of AI solutions using Claude Enterprise (Claude Code), (Microsoft Copilot Studio, Microsoft Foundry Azure AI Services), and LLM/RAG frameworks.
- Manage the AI portfolio from intake through value measurement, applying build-versus-buy-versus-partner discipline and retiring solutions that do not deliver.
- Ensure AI use cases are risk-classified and approved before reaching production.
- Partner with the Director of IT to support AI tools in production and manage helpdesk escalation.
Agentic AI & Agent Harness Engineering:
- Define the enterprise reference architecture for agentic AI: agent design patterns, orchestration, tool and API integration (including Model Context Protocol), memory, and retrieval.
- Own the agent harness: shared runtime guardrails, evaluation and regression testing, tracing and observability, prompt and configuration management, cost and rate controls, and human-in-the-loop approval gates.
- Establish identity, least-privilege access, and audit logging for agents, treating agents as governed non-human identities with a registry and lifecycle management.
- Set autonomy tiers that define what agents may do independently versus with approval, and monitor agent behavior in production.
Automation & Intelligent Process Improvement:
- Lead the enterprise automation strategy across workflow automation (e.g., Power Automate), robotic process automation, API integration, and agent-driven processes.
- Maintain an automation pipeline with intake, prioritization, ROI tracking, and support standards, and coordinate with business process owners to redesign processes before automating them.
Data & AI Enablement:
- Lead data and AI enablement: self-service analytics, certified datasets, reusable templates, and a champions network across business units.
- Deliver training, data and AI literacy programs, responsible-use guidance, and a community of practice so employees can use approved tools safely and effectively.
- Measure adoption and business impact, and lead change management for new data, AI, and automation capabilities.
Software Engineering Oversight:
- Lead the Engineering & AI Solutions team: custom software development, legacy modernization, DevOps, and AI solution delivery.
- Champion agile delivery practices, API design standards, secure development (DevSecOps), and CI/CD pipeline governance.
- Set standards for the responsible use of AI-assisted development tools and code review.
- Partner with the Director of IT on shared Azure tenant governance and DevOps standards.
Budget, Vendor & Team Leadership:
- Develop and manage the AI & Data operating and capital budgets, including cloud consumption, licensing, and capacity forecasting.
- Manage strategic vendors, consulting partners, and platform relationships, including contract terms, security and data-use requirements, and performance.
- Recruit, develop, and retain a high-performing team of data engineers, platform engineers, software engineers, automation developers, and AI specialists, with defined career paths and succession coverage.
- Poster a culture of rigor, experimentation within guardrails, documentation, and continual learning.
- Performs all other duties as assigned.
Qualifications
Education:
- Bachelor’s degree in Computer Science, Data Science, Information Systems, or a related technical field required.
- Master’s degree preferred.
Experience:
- Minimum of 10 years of progressive technology leadership with depth in data engineering, AI/ML, or software development, including at least 5 years leading multi-disciplinary teams and managing budgets.
- Demonstrated experience building and operating data platforms (Microsoft Fabric, Databricks, Snowflake, or similar) at scale using a medallion or equivalent layered architecture.
- Proven track record deploying and governing AI solutions in a production enterprise environment, including LLM/RAG solutions; hands-on experience with agentic AI is strongly preferred.
- Experience establishing data governance and AI governance programs, not only operating within them.
- Experience in equipment dealership, heavy equipment, manufacturing, distribution, or other asset-intensive industries preferred.
Technical Knowledge:
- Deep familiarity with Microsoft Fabric (Lakehouse, Warehouse, Data Factory, Real-Time Intelligence, Direct Lake), OneLake, Power BI, and Azure AI Services / Microsoft Foundry.
- Strong working knowledge of data engineering practices: SQL, Python, Spark, orchestration, testing, observability, and DataOps.
- Understanding of software development lifecycles, CI/CD, DevSecOps, infrastructure as code, and agile delivery.
- Knowledge of cloud security and identity (e.g., Microsoft Entra ID), role-based access, and cost management in Azure.
Data Governance & Responsible AI Knowledge:
- Knowledge of data governance, cataloging, lineage, and master data management (e.g., Microsoft Purview).
- Knowledge of AI governance frameworks (e.g., NIST AI RMF, ISO/IEC 42001) and applicable data privacy and AI regulations.
- Ability to translate risk and compliance requirements into practical, enforceable standards.
Agentic AI & Automation Knowledge:
- Understanding agent architectures, orchestration and tool-use patterns, Model Context Protocol, retrieval and vector search, and evaluation and tracing approaches for LLM systems.
- Familiarity with agent harness concepts: guardrails, permissions, human-in-the-loop controls, and monitoring of autonomous workflows.
- Familiarity with workflow automation and RPA platforms (e.g., Power Automate) and with integrating automation with AI agents.
Leadership & Organizational Design:
- Experience designing and scaling a center of excellence or shared-services organization, including operating models, intake processes, and service levels.
- Experience building enablement and adoption programs and leading organizational change.
- Ability to attract, develop, and retain scarce data and AI talent and build a succession-ready team.
Skills and Competencies:
- Strategic thinker with the ability to communicate complex technical concepts to executives and to link investments to business outcomes.
- Strong collaboration and influencing skills; experience operating in a peer-leadership model.
- Sound judgment in balancing speed and innovation against risk, security, and cost.
- Vendor management and negotiation skills.
Certifications (Preferred):
- Microsoft Certified: Fabric Data Engineer Associate, Fabric Analytics Engineer Associate, Azure AI Engineer Associate, or Azure Data Engineer Associate.
- Governance or leadership credentials such as CDMP, IAPP AIGP, ISO/IEC 42001 Lead Implementer, or PMP are a plus.
__________________________
Equal Opportunity Employer/Protected Veterans/Individuals with Disabilities. The contractor will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant. However, employees who have access to the compensation information of other employees or applicants as a part of their essential job functions cannot disclose the pay of other employees or applicants to individuals who do not otherwise have access to compensation information, unless the disclosure is (a) in response to a formal complaint or charge, (b) in furtherance of an investigation, proceeding, hearing, or action, including an investigation conducted by the employer, or (c) consistent with the contractor’s legal duty to furnish information. 41 CFR 60-1.35(c).
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 Wheeler Machinery Co, 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. Director-level AI roles across all categories have a median of $272,300.
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.
Wheeler Machinery Co AI Hiring
Wheeler Machinery Co has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Salt Lake City, UT, US.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.