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About This Role
We are seeking a Director of AI & Automation to lead the design, development, and deployment of a company-wide automation and AI strategy. This role will be responsible for transforming how our business operates by implementing intelligent systems, workflow automation, and AI-driven processes across all departments. This is a hands-on leadership role for a builder who can both architect and execute.
The ideal candidate will drive the implementation of AI agents, workflow automation, and system integrations to improve efficiency, reduce manual labor, and scale operations. This is not a purely managerial role. The ideal candidate must be capable of designing systems, understanding technical architecture, and working closely with developers to implement solutions.
Responsibilities:AI & Automation Strategy
- Develop and execute a company-wide AI and automation roadmap
- Identify high-impact use cases across all departments
- Prioritize initiatives based on ROI, scalability, and operational impact
Architecture Ownership
- Define the system architecture for all AI and automation initiatives
- Establish standards for data flow, integrations, and system design
- Determine how AI agents interact with business systems
- Ensure scalability, reusability, and consistency across automation projects
- Evaluate and approve technical design decisions across all implementations
System Architecture & Integration
- Design integration between Salesforce, Teams, SharePoint, Power Automate, and AI platforms
- Oversee API integrations and data synchronization
- Ensure consistency and reliability across systems
AI Agent & Workflow Development
- Lead development of AI-driven workflows and agent-based systems
- Automate onboarding, submittals, purchasing, and sales processes
- Determine when to use rule-based vs. AI-based automation
Execution & Delivery
- Build and deploy solutions rapidly with a bias toward execution
- Collaborate with internal developers and external vendors
- Create reusable automation frameworks and templates
Cross-Department Leadership
- Work with department heads to identify automation opportunities
- Drive adoption of automation tools and processes
- Train teams on new systems
Qualifications:Required Qualifications
- 5+ years in automation, systems integration, or AI implementation
- Strong understanding of system architecture and integrations
- Experience with Salesforce or other CRM systems
- Experience with APIs and multi-system workflows
- Ability to design scalable solutions across multiple platforms
Preferred (Not Required, but a Plus)
- Experience with UiPath, Microsoft Power Automate, or Salesforce Agentforce
- Experience with AI platforms (OpenAI, Gemini, Claude)
- Experience in manufacturing, construction, or industrial environments
What We're Looking For
- Builder mindset, not just a strategist
- Strong systems thinker
- Ability to move fast and execute without overengineering
- High accountability and ownership
Physical Demands
The physical demands described here represent those that must be met to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Regularly works at a computer for extended periods, reviewing data, code, dashboards, and documents on screen.
- Regularly communicates with team members, stakeholders, and vendors in person, by phone, and by video conference. Exchanging information accurately is essential.
- Occasionally moves around the office and between meeting rooms. Occasionally travels to company sites, client sites, or industry events.
- Occasionally moves office items such as a laptop or presentation materials, typically up to 15 lbs.
- Remains in a stationary position (seated or standing) for much of the workday.
Work Environment
Work takes place mainly in a professional office setting with a typical noise level. The role is on-site at 11400 N Jog Road, Suite 200, Palm Beach Gardens, FL, 5 days per week. Occasional evening or weekend work may be needed to meet project deadlines or support critical deployments.
ADA Compliance Statement
The Company is committed to providing equal employment opportunities to qualified individuals with disabilities. Reasonable accommodations will be provided to qualified individuals with disabilities to enable them to perform the essential functions of the position unless doing so would create an undue hardship on the Company.
About Company
Meta Power Solutions is a leading manufacturer of oil-filled power and distribution transformers, low-voltage switchboards, medium-voltage switchgear, and containerized substations in North America. We design and build all of our own products in-house. The industries we serve include utilities, data centers, oil and gas, commercial construction, solar, and bitcoin mining.
Benefits:
- 401(k)
- Dental insurance
- Health insurance
- Paid time off
- Vision insurance
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Meta Power Solutions, 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Meta Power Solutions AI Hiring
Meta Power Solutions has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palm Beach Gardens, FL, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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