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Position Summary
The Director of Technology & AI Automation oversees FEI's technology infrastructure and leads the identification, implementation, and adoption of AI and automation solutions across the institution. This role bridges traditional IT operations (systems, networks, data security) with practical AI deployment, including building and running the AI automation layer that supports enrollment marketing and student outreach. The Director ensures all technology is reliable, secure, and aligned with FEI's operational, marketing, and compliance requirements.
Duties & Responsibilities
Technology Operations
- Oversee day-to-day IT operations, including systems, networks, and data security.
- Ensure institutional data systems (SIS, CRM, LMS) are integrated and functioning reliably, including integrations needed for marketing AI tools.
- Develop and enforce data security and privacy practices, including for AI tool usage.
- Manage vendor relationships for IT infrastructure, SIS, and AI/automation platforms, and coordinate with vendors and technical support to resolve issues.
- Manage the technology budget, including AI tool licensing and infrastructure costs.
- Supervise IT and automation support staff or contractors.
AI Implementation
- Lead the planning and rollout of AI and automation initiatives across departments.
- Partner with department leaders (Marketing, Admissions, Financial Aid, Academics) to assess workflows and identify processes suited for automation.
- Research and recommend off-the-shelf or lightly customized AI solutions.
- Lead implementation projects from pilot through full deployment.
- Maintain an inventory of active AI tools and their institutional use cases.
Marketing & Enrollment Automation
- Build and manage AI agent workflows for inbound and outbound admissions and marketing outreach.
- Configure multilingual AI scheduling for admissions appointments and information sessions.
- Use AI tools to support social media content planning and website content creation.
- Deploy AI-driven surveys to gather student and prospect feedback.
- Work with the CMO to align AI automation efforts with enrollment marketing goals.
- Monitor AI agent performance and refine scripts, prompts, and workflows over time.
Training, Reporting & Compliance
- Develop training materials and lead onboarding sessions for staff using new AI tools.
- Measure and report on the impact of deployed AI solutions (efficiency, response time, conversion).
- Ensure technology, AI practices, and AI-generated content comply with applicable federal, state, and accreditor requirements and FEI brand standards.
Position Requirements
Education
- Bachelor's degree in Information Technology, Computer Science, Information Systems, Marketing, or related field required; Master's preferred.
Experience
- 5+ years in IT management, technology operations, technology implementation, or marketing automation, including team or vendor oversight.
- Demonstrated experience rolling out software or AI tools to non-technical teams.
- Experience with AI agent, chatbot, or AI content tools strongly preferred.
- Higher education or regulated-industry experience a plus.
Technology
- Proficient with common workplace technology and software, including IT infrastructure management, SIS/CRM/LMS platforms, AI agent and chatbot platforms, marketing automation tools, content creation software, and project management software.
- Comfortable learning and using institutional systems related to admissions, financial aid, and student records.
- Adapts readily to new software and technology as institutional systems evolve.
Skills & Attributes
- Strong leadership, project management, and cross-departmental coordination skills.
- Strategic thinker able to balance innovation with operational stability.
- Strong writing and content sensibility for student-facing communication.
- Comfortable testing and iterating on AI prompts and scripts.
- Analytical mindset with the ability to translate results into plain-language reporting.
- Patient, effective trainer for staff at varying technical comfort levels.
- Sound judgment on data security and regulatory compliance.
Pay: $100,000.00 - $125,000.00 per year
Benefits:
- Dental insurance
- Flexible schedule
- Health insurance
- Paid time off
- Professional development assistance
- Retirement plan
- Vision insurance
Work Location: In person
Salary Context
This $100K-$125K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At FLORIDA EDUCATION INSTITUTE, 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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($112K) sits 47% below the category median. Disclosed range: $100K to $125K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
FLORIDA EDUCATION INSTITUTE AI Hiring
FLORIDA EDUCATION INSTITUTE has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in West Miami, FL, US. Compensation range: $75K - $125K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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