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Implementation Consultant – AI Service Applications
Department: Professional Services Reports To: Senior Director, Professional Services Employment Type: Full-Time ABOUT US
We are dealer focused and OEM aligned. We are a leading ERP & DMS software and solutions provider to dealers and rental companies of heavy equipment. We specialize in working with the construction, mining, forestry, material handling, and agriculture industries. Today, we have over 800 employees, offices on four continents, and customers in over 20 countries. We are privately held, and our headquarters are in beautiful Cary, NC. We are actively seeking talented individuals to join our team and help us aggressively grow our international & North American footprint for both our 100% cloud-based ERP, DMS, and Rental solutions. WHY WORK FOR VITALEDGE
We don't just sell technology, we enable transformation that results in broader societal benefits like building homes and critical infrastructure, growing food and delivering all sorts of products we all rely on for daily life. We exist to ultimately equip the world to keep running. We have more than 60 years of combined experience and three industry-leading software suites and associated apps, with which we will drive the market forward. It's an exciting time to work for VitalEdge, join us! POSITION OVERVIEW
We are looking for a hybrid technical and functional specialist to lead the implementation and ongoing management of our new AI Technician Assist platform. The platform analyzes historical service data, repair patterns, failure codes, and OEM resources to help field and shop technicians reach a faster, more accurate diagnosis of a customer-reported machine failure. The role sits at the intersection of AI technology, our e-Emphasys ERP service data, and real-world equipment service operations. The ideal candidate can speak credibly with an AI product team, read our ERP service data structures, and just as importantly, understand how a technician actually works through a failure in the field. RESPONSIBILITIES
Solution Implementation: Lead the end-to-end implementation of the AI Technician Assist platform, covering configuration, data integration, pilot rollout, and scaling across service centers, partnering with IT, Service Operations, and the e-Emphasys ERP Product team.
ERP & Data Integration: Own the integration between the AI Assist platform and the e-Emphasys ERP Service module (service orders, machine/unit history, warranty, parts, labor, and task codes) so the platform can pull accurate repair and failure history for root-cause analysis.
AI Performance & Feedback: Partner with the data science/AI Product team to define success criteria, validate model output against actual repair outcomes, and run a standing feedback loop from dealer technicians back to Product engineering.
Technician Workflow Design: Translate real-world troubleshooting workflows (symptom, diagnostic steps, root cause, repair) into the platform's guided assist flows.
Change Management & Training: Develop training materials and SOPs, run pilot programs, and drive adoption across a technician workforce with varying levels of digital comfort.
Stakeholder Communication: Report progress, risks, and adoption metrics to Service Operations leadership, IT, and the Product team. REQUIRED SKILLS & EXPERIENCE Technical / AI & ERP* 3+ years of hands-on experience with AI/ML-enabled applications, including how recommendation, natural language, or diagnostic/decision-support models are built, trained, and evaluated. A data science background is not required, but the candidate must be conversant with AI capabilities, limitations, and data requirements.
- Working knowledge of a heavy equipment dealer ERP (e-Emphasys, Infor, or SAP), specifically the Service module, and how that data can be extracted or integrated via APIs.
- Experience with system integration concepts (APIs, data pipelines, middleware) sufficient to read data models and troubleshoot integration issues alongside developers. Production coding is not required.
- Familiarity with data quality and governance practices, ensuring the historical service data feeding the AI model is clean, complete, and properly mapped.
Functional / Service & Technician Perspective* Solid functional understanding of heavy equipment dealer operations from a Service and Parts perspective, including service order lifecycle, warranty claims, technician dispatch, and shop/field service workflows.
- Direct familiarity with how field and shop technicians diagnose equipment failures, including troubleshooting logic, use of OEM manuals and bulletins, and common root-cause categories (mechanical, electrical, hydraulic).
Education & Experience* Bachelor's degree in a related field (Computer Science, Information Systems, Engineering, or equivalent practical experience), or equivalent combination of education and hands-on service/technology experience.
- 5+ years of combined experience across ERP/service systems, technology implementation, and/or heavy equipment service operations.
PREFERRED QUALIFICATIONS* Prior experience at a heavy equipment dealer, OEM, or industrial service organization (construction, agriculture, mining, or drilling equipment).
- Experience working directly with an AI/software vendor on a pilot-to-scale product rollout.
- Prior experience with service technology rollouts such as mobile service apps, diagnostic tools, or telematics.
- Familiarity with change management frameworks for technology adoption among a frontline, non-desk workforce.
WHAT SUCCESS LOOKS LIKE* Successful pilot and phased rollout of the AI Technician Assist platform across targeted service centers, on schedule.
- Measurable improvement in diagnostic accuracy and reduction in average diagnostic/repair time for participating technicians.
- Improved first-time-fix rate and reduction in repeat visits for the same complaint.
- Technician adoption and satisfaction, meaning the tool is actively used and trusted in the field, not bypassed.
- Clean, well-integrated data pipeline between the e-Emphasys ERP and the AI platform, with minimal manual data wrangling.
Salary Context
This $130K-$150K range is below the median 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 E-Emphasys Technologies Inc, 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 ($140K) sits 34% below the category median. Disclosed range: $130K to $150K.
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.
E-Emphasys Technologies Inc AI Hiring
E-Emphasys Technologies Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Cary, NC, US. Compensation range: $150K - $150K.
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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