Interested in this AI/ML Engineer role at WM Synergy?
Apply Now →Skills & Technologies
About This Role
Position Description:
About Us
WM Synergy is a 40\-year ERP and business transformation partner for SMB manufacturers and distributors, executing a strategic pivot from an ERP services firm to an outcomes company powered by ERP \+ AI. We're looking for a Director of AI to shape and lead our AI practice as we make that shift.
About the Role
This is a builder role. You'll partner with the CEO, CSO, and executive team to set the AI vision, then run the practice that delivers against it — leading a small, focused team, coaching our consultants and engineers, and turning deep manufacturing and distribution expertise into productized AI offerings that customers pay for. The foundation is already in place (AI Council, Practitioner Academy, 100\+ internal skills, active tooling, and an approved budget), but the path forward is still being shaped, and we need a leader to help shape it. About You
You ship production agents, not demos. You bring your own point of view on where AI is going in mid\-market ERP, you can architect and write code rather than just delegate, and you have the commercial instincts to turn internal capability into a defensible revenue line. As the practice scales and delivers, this role grows into VP, AI Strategy \& Practice or Chief AI Officer.
Position Responsibilities:
Vision \& Strategic Partnership
Partner with the CEO, CSO, and ELT to shape and continuously refine the AI strategy. Bring your own point of view on where AI is going in mid\-market ERP — where the moat is and where to place bets — and translate market and customer signals into a roadmap the team can execute against. You are our eyes on the AI landscape; push back when we're missing something.
Team Leadership
Lead a small, focused AI team and hire 1–2 business analysts, agent engineers, or AI experts in year one, adding fractional specialists as needed. Coach our consultants and engineers as they learn to build agents, ERP customizations, and productized IP grounded in their industry expertise. Run the AI Practitioner Academy and grow the Skill Library so AI fluency becomes a baseline expectation, not a specialization. Customer Feedback \& Productized Agents
Run the customer feedback loop — sit in on flagship engagements, listen for what customers actually need, and feed it back into the roadmap. Deliver the productized agent roadmap, building 3–5 industry\-specific agents in year one tied to manufacturing and distribution workflows, and partner with Sales and Delivery on customer\-facing AI engagements from scoping through deployment. AI Spend Management (FinOps)
Own all internal AI spend, building cost dashboards, model\-routing policies, and consumption governance. Establish token discipline as a team practice and build AI FinOps into an external service line — assessment, governance, model selection, value tracking — turning internal discipline into recurring customer revenue. Idea\-to\-Production Factory
Run the intake\-to\-production pipeline in partnership with the AI Council: prototype, evaluate, productize, retire. Kill quickly, scale what works, and maintain the submission process, value\-per\-token forecasts, and reliability standards. Tool, Architecture \& Reliability Standards
Own the decision framework for when to use Claude/CoWork, Velocity Suite, Acumatica's native AI, or open\-weight models, and maintain architecture standards across the team. Make reliability an economic discipline: every agent ships with bounded loops, evaluation criteria, and human\-in\-the\-loop checkpoints.
Performance Measurement: What Success Looks Like (12–18 Months)
- A shared, articulated vision for the AI practice that the team, the ELT, and our customers can repeat back to you.
- 100% of consultants badged in the AI Foundations Track, with the Skill Library at 200\+ skills.
- 3–5 productized agents in market, with named flagship reference customers.
- An AI FinOps offering stood up internally, proven out, and landing its first customers.
- Internal AI spend tracked, attributable, and under approved budget.
- A team you've built that's capable, focused, and ready to scale — and you're ready to grow into a VP or Chief AI role as the practice expands.
Knowledge, Skills and Abilities: Knowledge \-
- Working knowledge of the AI agent landscape — agent frameworks, RAG and retrieval, and evaluation tooling — and how to apply it to real business problems.
- Command of AI cost economics: token economics, model tiering and routing, caching, and consumption tied to a real budget.
- Understanding of SMB or mid\-market services delivery, ideally in manufacturing, distribution, ERP, or adjacent industries.
- Familiarity with the reliability basics every production agent needs: bounded loops, evaluation criteria, monitoring, and human\-in\-the\-loop checkpoints.
Skills \-
- Hands\-on AI building. Direct experience with Claude/Anthropic, agent frameworks, RAG, and evaluation systems — you can architect and ship, not just delegate.
- Able to read and write code and make the architecture calls, including wiring agents into ERP, CRM, and operational systems through clean APIs and trustworthy data.
- Team leadership and coaching across both technical and functional disciplines, developing capability in others rather than hoarding it.
- Strong commercial instincts — comfortable with margin profiles, pricing conversations, and customer\-facing engagements.
Abilities \-
- Both strategic and hands\-on — able to shape a vision and ship the work that proves it out.
- Customer\-obsessed, with the judgment to know when to listen and when to push back.
- Strong opinions, loosely held: brings a clear point of view but listens hard and updates fast.
- Direct, accountable, low\-ego, and high\-transparency, with the ambition to grow the role as the practice scales.
Physical Requirements: The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this role. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Prolonged periods of sitting at a desk and working on a computer.
- Regular use of hands and fingers to operate a computer, keyboard, and other office equipment.
- Ability to communicate clearly and exchange accurate information consistently, in person and through virtual channels.
- Occasional standing, walking, bending, and reaching.
- Ability to travel regularly to customer and company sites, including occasional overnight or air travel.
- Must be able to lift up to 15 pounds occasionally (e.g., laptop bag, materials for travel or on\-site work).
Salary Context
This $200K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At WM Synergy, 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. Director-level AI roles across all categories have a median of $272,150. Disclosed range: $200K to $240K.
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.
WM Synergy AI Hiring
WM Synergy has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in TX, US. Compensation range: $240K - $240K.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.