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About This Role
Who We Are:
Join GTIA. Help shape the future of the global IT channel.
The IT channel powers how businesses worldwide run, stay protected, and grow. GTIA exists to make that channel stronger. Our mission is to advance the people and businesses of the global IT channel with trusted, unbiased resources as an inclusive community that drives growth.
We are the only vendor-neutral, non-profit membership community connecting and representing the worldwide IT channel. Our more than 3,000 corporate members include IT solution providers, MSPs, vendors, distributors, and other companies, representing tens of thousands of professionals, with communities and events across North America, EMEA, and ASEAN. Through education, training, events, and market research, we help them build sustainable, secure, and profitable businesses. And we're growing fast.
Our impact reaches beyond the industry. Through the GTIA Foundation, we support the communities where our members live and work, with $5 million in donations and grants awarded in 2026.
Our story runs deep. We launched as GTIA in 2025, carrying forward a long history of serving the IT channel as the former CompTIA Community. Now we're writing the next chapter, and we want you to help write it.
Life at GTIA is shaped by our core values:
- Member First: Start every idea with the people we serve.
- Growth Minded: Learn, stretch, and take on new challenges.
- Build Together: Collaborate across teams, time zones, and regions.
- Share the Vibe: Celebrate every win, big and small.
What You'll Get:
- A fully remote work environment
- Excellent health benefits
- Unlimited PTO
- Competitive salaries
- A strong incentive program
We're looking for more than employees. We're looking for partners who share our vision and want to make a real impact on a global stage. If you're ready to grow, contribute, and reach new heights, join us.
Where the IT channel connects and grows. #WeAreGTIA
Summary:
The Director, AI Member Resources leads GTIA’s member-facing AI strategy, developing and operationalizing AI-driven resources, education, and member enablement resources that help IT solution providers (ITSPs) leverage artificial intelligence securely, effectively, and profitably. Working in collaboration with internal teams, member AI groups, and GTIA’s Managed Intelligence Alliance, the Director delivers and manages programming focused on AI adoption, AI security, governance, and industry enablement for members, and serves as a trusted internal AI resource for GTIA staff. This role bridges GTIA’s cybersecurity expertise with emerging AI opportunities, ensuring AI is adopted securely and responsibly to create practical business outcomes for members and for GTIA, in support of GTIA’s mission to advance the people and businesses of the global IT channel with trusted, unbiased resources. This position reports to GTIA’s Chief Community Officer.
Essential Duties and Responsibilities
Strategy and Program Development
- Develop GTIA’s AI resource strategy and member-facing AI enablement programs.
- Co-lead the development and implementation of the GTIA AI & Cyber Resource Center
- Collaborate closely with Member Education, Research & Market Intelligence, and the Managed Intelligence Alliance to create models, assessments, and readiness pathways for ITSPs.
AI Security and Risk Guidance
- Ensure member resources combine AI implementation guidance with cybersecurity best practices.
- Monitor emerging AI risks, such as prompt injection, data leakage, model and AI supply-chain risk, AI-enabled phishing and social engineering, and unsanctioned (“shadow”) AI use, and translate them into timely member guidance and advisories.
Content and Community
- Develop toolkits, guides, webinars, podcasts, workshops, and thought leadership content in close coordination with Marketing.
- Work with volunteer members, industry experts, and strategic partners to build resources and programs while maintaining GTIA’s unbiased position.
Internal Enablement and Representation
- Partner with IT as a resource for internal AI usage, governance, and acceptable use.
- Represent GTIA at conferences and industry events as an AI and AI security thought leader.
Key Performance Metrics:
- Within the first 12 months, the GTIA AI & Cyber Resource Center is launched, an initial portfolio of AI member resources is in market, and a regular cadence of AI security guidance is established.
- Member adoption of AI resources, measured by growth in downloads, views, and usage.
- Industry engagement, measured by attendance and active participation in AI communities and events.
- Growth of AI member resources, measured by number of programs offered and enrollment.
- Impact of AI resources on member business outcomes, measured through testimonials, surveys, and video case studies.
- Timely publication of AI security guidance and threat advisories in response to emerging risks.
Skills and Qualifications:
- Hands-on proficiency with AI platforms, large language models (LLMs), AI agents, prompt engineering, and automation workflows.
- Proficiency in cybersecurity governance and risk management, including AI-specific security risks.
- Working knowledge of data privacy and responsible AI principles, including data handling, bias, transparency, and emerging AI regulation.
- Proficiency in facilitating volunteer member groups.
- Proficiency with Microsoft 365 (Excel, PowerPoint, Word, Copilot) and Canva.
- Experience with HubSpot or another CRM.
- Strong public speaking and presentation skills.
- Superior customer and member service ethic.
- Excellent interpersonal, written, and oral communication skills.
- Proactive, intellectually curious, self-driven learner who stays current on the rapidly evolving AI and threat landscape.
- Ability to work independently and within cross-functional teams.
- Exceptional administrative, organizational, and time management skills, with the ability to prioritize multiple assignments and meet deadlines.
- Outstanding attention to detail and quality.
Education and/or Experience:
- Degree preferred but not required.
- 7+ years of experience in cybersecurity, AI/automation, IT services, or a related field, ideally within the IT channel or ITSP/MSP community.
- Demonstrated experience developing AI or cybersecurity programs, education, or resources.
Work Environment:
GTIA is fully remote, and this is a fully remote position.
- Travel is required 30–50% of the time, including conferences, industry events, and member engagements.
GTIA seeks excellence through diversity in its staff. We prohibit discrimination based on race, color, religion, sex, age, national origin, sexual orientation, gender identity or expression, disability, veteran status, or marital status.
Salary Range: Up to $175,000 Total Compensation (Base + Bonus)
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At GTIA, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
GTIA AI Hiring
GTIA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $209,100 across 306 tracked positions.
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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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