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Why Ryan?
- Hybrid Work Options
- Award\-Winning Culture
- Generous Personal Time Off (PTO) Benefits
- 14\-Weeks of 100% Paid Leave for New Parents (Adoption Included)
- Monthly Gym Membership Reimbursement OR Gym Equipment Reimbursement
- Benefits Eligibility Effective Day One
- 401K with Employer Match
- Tuition Reimbursement After One Year of Service
- Fertility Assistance Program
- Four\-Week Company\-Paid Sabbatical Eligibility After Five Years of Service
The Director, GSS (Corporate) AI Initiatives is responsible for identifying, prioritizing, and executing AI\-driven initiatives across Ryan’s global corporate support functions, including Finance, Legal, Marketing, HR, and other centralized teams.
This role owns a portfolio of AI initiatives from intake through delivery, ensuring efforts are focused on high\-value use cases and produce measurable outcomes.
The Director partners closely with functional leadership, IT, and external vendors to translate business needs into practical AI solutions, while also driving adoption and building AI capability across the organization.*This is a hybrid role based in Plano, TX, with an expectation of working from the office 3–4 days per week.*
Duties and Responsibilities
People
- Lead cross\-functional teams to deliver AI initiatives, including coordination across business stakeholders, IT, data teams, and external partners
- Provide hands\-on guidance to functional leaders and individual contributors on applying AI tools effectively in their day\-to\-day work
- Design and coordinate AI training and enablement programs for corporate functions, including role\-based training, workshops, and ongoing support
- Establish a network of AI “power users” or champions within functions to accelerate adoption and scale knowledge, and lead this group through frequent meetings, ensuring creation of use cases, adoption of tracking processes, and execution of completed initiatives
Client (Internal)
- Partner with leaders across Finance, Legal, Marketing, HR, and other functions to identify and prioritize high\-impact AI opportunities
- Establish and manage a structured intake and prioritization process for AI initiatives based on value, feasibility, and alignment with firm priorities
- Serve as a practical advisor to functional teams on how AI can be applied to improve efficiency, quality, and scalability of their work
Value
- Own and manage a portfolio of AI initiatives, including business case development, prioritization, execution tracking, and value realization
- Lead execution of initiatives through internal resources and/or third\-party vendors, ensuring delivery against defined objectives
- Evaluate, select, and manage external vendors, platforms, and tools to support AI initiatives
- Define and implement a repeatable operating model for AI initiatives across corporate functions, including governance, prioritization, and delivery processes
- Ensure alignment with firm standards related to data security, confidentiality, and risk management while enabling practical implementation
- Track and report on outcomes, including efficiency gains, cost savings, and adoption metrics
Education and Experience
- Bachelor’s degree required; advanced degree preferred
- 10\+ years of experience in strategy, transformation, analytics, or technology roles
- Demonstrated experience leading cross\-functional initiatives in a complex, global organization
- Experience working with or implementing AI/ML or Generative AI solutions in business environments
- Experience managing third\-party vendors or technology partners
- Experience in professional services or corporate support functions strongly preferred
Required Skills
- Ability to translate business problems into clearly defined, actionable AI use cases
- Strong program and project management capabilities across multiple concurrent initiatives
- Ability to work effectively across functions in a matrixed environment
- Strong communication skills, including the ability to work directly with senior leadership
- Experience establishing or operating an AI or digital transformation function
- Familiarity with AI governance and risk considerations
- Practical understanding of AI capabilities and limitations
- Experience evaluating and selecting technology vendors
- Ability to balance strategic prioritization with hands\-on execution
Preferred Skills
- Experience designing and delivering training or enablement programs
- Experience in a global, professional services environment
Success Metrics / KPIs – First 12 Months
Portfolio \& Delivery
- Establish a prioritized AI initiative portfolio across corporate functions within first 90 days
- Deliver 3–5 high\-impact AI initiatives to production with measurable outcomes
Achieve on\-time delivery for* 80% of approved initiatives
Value Realization
- Demonstrate quantifiable efficiency gains or cost savings from delivered initiatives (e.g., time reduction, automation impact)
- Implement a consistent framework for tracking ROI across all AI initiatives
- Deliver at least 1–2 scaled use cases per major function (Finance, HR, Marketing, etc.)
Adoption \& Enablement
- Achieve targeted adoption metrics for AI tools within corporate functions (e.g., % of users actively leveraging tools monthly)
- Design and roll out role\-based AI training programs across corporate functions within first 6 months
- Train a defined percentage of employees within corporate functions (e.g., 50%\+) on approved AI tools and use cases
- Establish and activate an AI champions network within key functions
Operating Model \& Governance
- Stand up a repeatable intake, prioritization, and governance process for AI initiatives within first 90–120 days
- Define and implement standards for vendor selection, use case approval, and risk alignment
- Establish regular reporting cadence on AI portfolio performance and outcomes
Stakeholder Impact
- Achieve strong alignment and engagement with functional leadership, measured through adoption and participation
- Be recognized as a go\-to resource for practical AI application within corporate functions
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 Ryan, LLC, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554.
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.
Ryan, LLC AI Hiring
Ryan, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
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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