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About This Role
At GAF, we cover more than buildings. We cover each other. No matter what role, tenure, or track, under this roof you are empowered to be there for your teammates, your customers, and especially your community. Under this roof, we don’t back down from hard work– we support one another in pursuit of something bigger. We define the future while leading the present. And under this roof, we own our opportunities. Becoming the market leader only happens when everyone feels they have the opportunity, and the support, to thrive. We are GAF. And under this roof, we protect what matters most.
Job Summary
The Director of AI and Intelligent Automation is a critical leadership role responsible for architecting and executing the enterprise\-wide AI journey. Reporting directly to the VP of Data and AI, this individual will own the AI execution strategy, business alignment, and portfolio prioritization and delivery approach for the entire organization in partnership with different departments. The primary objective or the role is to transform the company to use AI technology to be more effective and efficient in delivering the business strategies \- from individual productivity aids to robust, automated enterprise solutions that drive systemic efficiency and measurable financial returns.
Essential Duties
- Vision and Execution Strategy
+ Define and own the execution strategy for the enterprise AI roadmap, ensuring alignment with the overarching vision to position AI as a "Technology Multiplier" for people and processes.
+ Facilitate alignments and deliverables on AI automation across all business functions
+ Manage the full innovation lifecycle: from initial "Culture Core" engagement to scaled "Enterprise Deployment" of custom Agentic workflows.
- Solution Design \& Engineering Oversight
+ Oversee the development and deployment of Generative \& Agentic AI solutions, including custom RAG (Retrieval\-Augmented Generation) repositories and autonomous multi\-agent workflows.
+ Guide the technical team in building robust Computational AI models, such as simulations for R\&D, supply chain, and manufacturing optimizations.
+ Establish reusable technical patterns and platform controls to ensure scalable and reliable AI operations.
- AI Governance \& Risk Management
+ Partner with Cyber Security, Legal, and Procurement to establish and enforce robust governance frameworks for responsible AI adoption.
+ Lead the evaluation of third\-party AI solutions through comprehensive questionnaires to ensure compliance with enterprise security and data privacy standards.
+ Define success metrics and KPIs for AI implementations, monitoring performance, user adoption, and ROI.
+ Review AI use cases for viability and financial returns.
- Enablement \& Change Management
+ Co\-Launch and co\-lead the Data and AI Academy to provide role\-based, tiered education on AI practices and tools for the entire workforce.
+ Democratize innovation by managing the "Idea Collection Engine," systematically capturing and evaluating high\-ROI pain points directly from employees.
+ Act as the critical bridge between technical engineering teams and non\-technical business stakeholders.
Qualifications Required
- Bachelor’s Degree is required
- 10 \+ years relevant experience
- Technical Expertise
- Advanced AI Literacy: Deep conceptual and practical understanding of LLMs, Retrieval\-Augmented Generation (RAG), and agentic orchestration frameworks.
- Programming \& Frameworks: Proficiency and experience with orchestration tools such as LangChain, LlamaIndex, or AutoGen.
- Architectural Knowledge: Familiarity with vector search and storage solutions (e.g., Vertex AI Search) and API integration strategies.
- Leadership \& Strategy Architectural Knowledge: Familiarity with vector search and storage solutions (e.g., Vertex AI Search) and API integration strategies.
- Lead: Effectively lead a team of AI process engineers and builders including the ability to define and manage budgets and demand vs capacity. Influence thought leadership and work across all functions to drive and enable AI use cases.
- Business Analysis: Exceptional ability to map complex business processes and identify viable candidates for AI integration.
- Agile Methodology: Experience managing project backlogs, writing robust technical documentation, and leading cross\-functional teams in a fast\-paced environment.
- Communication: Proven ability to translate highly technical concepts for executive audiences and non\-technical business units.
General Knowledge, Skills and Abilities
- Future Roadmap Focus
The Director will lead the transition into 2026\+ Plans, which include:
- Expanding the availability of AI agents across various domains for both interactive and programmatic users.
- Establishing native AI connections with core enterprise applications such as SAP and Salesforce
- Scaling AI\-driven predictive insights and simulations from POC to fully active, high\-impact business products.
Base salary and/or rate of pay ranges listed are exclusive of fringe benefits and potential bonuses. Individual compensation offers will be determined based on a variety of factors, including but not limited to geographic location, relevant candidate experience and skill, education, and/or qualifications.
Base Salary Range: $176,000\-$242,000How We Protect What Matters Most:
1\. We offer a wide range of health insurance options that include medical, dental, and vision for you and your family. 2\. Our Family\-Building benefits support the many different journeys to fertility and parenthood. 3\. Our robust 401K plan includes an employer match contribution with your pre\-tax and/or Roth contributions. 4\. Other exciting programs and perks are available to help employees achieve work\-life balance, including (but not limited to) a wellness program, free financial coaching, a referral program, and product rebates when purchased for an employee’s primary residence. 5\. Professional growth and development are very important to us! We offer internal training programs and courses, as well as a generous tuition reimbursement program. 6\. We're committed to fostering a culture that reflects our values to connect, empower, evolve, and inspire. We offer many opportunities for employees to connect with one another, including through our Employee Resource Groups who focus on education and allyship for all of our employees.
GAF complies with federal, state, and local disability laws and makes reasonable accommodations for applicants and employees with disabilities. If a reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact HR Services at 833\-HR\-XPERT.
We believe our employees are our greatest resource. We offer competitive salary, benefits, 401k, and vacation packages for all full time permanent positions. We are proud to be an equal opportunity workplace. We are committed to equal employment opportunity on the basis of each candidate's qualifications, experience, and merit, without regard to race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, or Veteran status. If you have a disability or special need that requires accommodation, please let us know. If applying for positions in the U.S., must be eligible to work in the U.S. without need for employer sponsored visa (work permit).
Salary Context
This $176K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At GAF, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. Disclosed range: $176K to $242K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
GAF AI Hiring
GAF has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Parsippany-Troy Hills, NJ, US. Compensation range: $242K - $242K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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