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Position: Director of AI Strategy \& Enablement
Department: Information Technology
Reports to: VP of IT, Strategy and Innovation
Status: Exempt
Goodwill Keystone Area is committed to creating a culture of belonging where all people feel respected and valued. This is because we celebrate the diversity of thought, the richness of the human experience and the desire to reflect the communities we serve.
Summary
The Director of AI Strategy \& Enablement leads the design, governance, and enterprise\-wide adoption of Goodwill Keystone Area's artificial intelligence strategy. This role is a strategic investment in our mission, focused on creating new opportunities for workforce development, leveraging AI to enhance operational efficiency, and amplifying fundraising efforts. In addition, the position manages AI initiatives from opportunity assessment through deployment, leads workforce enablement efforts, and establishes governance and best practices to support responsible, scalable use of AI and automation.
Duties and Responsibilities
AI Strategy \& Roadmap
- Develop and own a sustainable, measurable enterprise AI strategy aligned to Goodwill Keystone's mission, with distinct plans for Retail/Donated Goods, Mission Services, and Business Services.
- Maintain a prioritized, resourced AI roadmap; lead the annual AI planning cycle with executive leadership.
- Frame problems, shape business cases, and define success metrics for each initiative before implementation begins.
- Monitor the external AI landscape – technologies, regulations, peer Goodwill affiliates, and nonprofit sector practice – and translate what matters into Goodwill Keystone's strategy.
AI Literacy \& Workforce Enablement
- Drive the expansion of AI Forge – Goodwill Keystone's existing AI literacy program – to build a sustainable and enablement enterprise\-wide model that focuses on: audience segmentation, role\-based learning paths, progression levels, and measurement of adoption and capability growth.
- Establish a community of practice for AI champions across Retail, Mission Services, and Business Services to accelerate peer learning and local application.
- Oversee selection and management of external training partners, platforms, manages higher level AI projects and content providers that complement the AI Forge.
Project Management \& Operational Implementation
- Work to identify, assist with prototype, and implement AI\-powered solutions to improve efficiency in core business areas that deliver clear measurable impacts (KPIs).
- Document current\-state business processes and define future\-state workflows as part of AI project scoping and implementation.
- Oversee management of AI initiatives to ensure timely testing, establishes benchmarks (KPIs) for and perform ROI assessments, leads teams’ go\-no go decisions (fail fast), and oversees the implementation and roll out.
- Ensure end users are actively involved in project and provided training to use new tools.
- Act as lead project manager on any grant projects that advance the use of new technologies.
Governance, Risk \& Responsible AI
- Establish and maintain AI governance policies covering data protection, privacy, bias monitoring, human oversight, vendor review, and acceptable use.
- Chair or convene an AI Review function (cross\-functional) to vet new AI use cases, tools, and vendor engagements.
- Ensure compliance with applicable regulations, funder requirements, and the data\-handling obligations of state and federal contracts in Business Services.
Portfolio, Vendor Oversight \& Enterprise Change
- Serve as Goodwill Keystone's primary relationship owner for external AI development partners and technology vendors.
- Assists in vendor selection, contracting, and accountability to scope, timeline, and measurable outcomes.
- Cultivate academic and nonprofit\-sector partnerships to access talent, research, and shared practice.
- Represent Goodwill Keystone selectively at industry and nonprofit forums when doing so advances the strategy; external thought leadership is a supporting activity.
- Promotes an environment of workplace safety. Ensures that duties are performed in a safe manner and safety requirements are adhered to.
Communicates progress, problems, and concerns to the VP of IT, Strategy and Innovation.
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Education and Experience
- Bachelor’s required; Equivalent professional experience will be considered.
- Five (5\) years prior experience in business process automation, workflow optimization, or solution development.
- Demonstrate experience developing and executing an AI strategy and base AI tools knowledge.
- Experience designing or leading workforce enablement, learning, or change management programs, ideally involving AI, data, or digital skills.
- Experience in project management.
- Experience establishing governance, policy, or risk frameworks for technology, data, or AI.
- Experience managing external vendors, consultants, or agency partners to delivery outcomes.
- Experience working in, with, or alongside mission\-driven, nonprofit, or social\-impact organizations strongly preferred.
Skills/Abilities/Qualifications
- Strong working understanding of the current AI landscape – generative AI, applied machine learning, automation, and responsible AI practice – sufficient to shape strategy, evaluate vendors, and guide internal decisions. Deep hands\-on engineering skills are not required.
- Understanding of data protection, privacy, and ethical considerations for AI, including bias and human\-in\-the\-loop design.
- Exceptional written and verbal communication; able to translate complex AI concepts into plain language for boards, executives, frontline staff, and community audiences.
- Strong critical thinking, problem\-solving, and project management skills are essential for managing multiple initiatives simultaneously.
- Structured strategic thinking; can define priorities, frame trade\-offs, and say no to work that is out of scope.
- Comfort designing and facilitating working sessions with senior leaders and organization staff.
- Comfort teaching, coaching, and building literacy in others, from executives to frontline staff.
- Comfort representing Goodwill Keystone in public forums.
- Ability to effectively work as part of a team or independently.
- Must have a modern reliable cell phone (Android or iOS).
- Must possess a valid driver’s license and reliable vehicle or reliable transportation.
- Ability to travel within Goodwill Keystone Area territory.
Goodwill Keystone Area is an Equal Opportunity Employer and is committed to complying with all federal, state, and local laws that prohibit discrimination in employment. We provide equal employment opportunities to all qualified individuals without regard to disability or status as a protected veteran.
Salary Context
This $101K-$124K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Goodwill Keystone Area, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($112K) sits 38% below the category median. Disclosed range: $101K to $124K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Goodwill Keystone Area AI Hiring
Goodwill Keystone Area has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Harrisburg, PA, US. Compensation range: $124K - $124K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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