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About This Role
Position Summary
We are at an inflection point. AI is no longer a future capability — it is the core operating system of competitive enterprise. As our Director of Artificial Intelligence, you will be the architect and catalyst behind how we design, build, and scale AI across the organization. From foundation models and autonomous agents to end\-to\-end business process automation, you will shape the intelligence layer of our enterprise.
This is not a research role or a proof\-of\-concept position. You will own outcomes — driving AI from strategy to production, bridging the gap between emerging capabilities and real business impact, and building the talent, platforms, and culture that make it all sustainable.What will your job entail?
WHAT YOU'LL OWN
AI Architecture \& Platform Leadership
- Define and own the enterprise AI architecture, spanning LLMs, SLMs, fine\-tuned models, and retrieval\-augmented systems.
- Establish model selection frameworks — knowing when to use a frontier model, a distilled small language model, or a purpose\-built fine\-tune.
- Design and govern AI infrastructure: vector databases, embedding pipelines, model registries, and inference optimization.
- Lead the adoption and governance of Model Context Protocols (MCPs) to standardize how agents interact with enterprise tools and data.
Agentic Systems \& Autonomous Workflows
- Architect multi\-agent systems capable of reasoning, planning, and executing across complex, multi\-step business processes.
- Design agent orchestration patterns — supervisor/worker hierarchies, tool\-calling protocols, memory management, and guardrails.
- Lead end\-to\-end business process automation using AI — from intake and classification to decision\-making, execution, and audit trails.
- Collaborate with process owners across Finance, Operations, HR, Legal, and Sales to identify and prioritize automation opportunities with measurable ROI.
Strategy, Governance \& Responsible AI
- Develop and maintain the enterprise AI roadmap, aligned to business strategy and refreshed quarterly with the VP of Engineering and executive stakeholders.
- Build and enforce AI governance frameworks: bias evaluation, hallucination mitigation, data privacy, model explainability, and regulatory compliance.
- Stay ahead of the curve — continuously evaluating frontier research, new model releases, and ecosystem tooling to maintain competitive advantage.
- Act as the internal AI thought leader — educating teams, presenting to leadership, and representing the organization externally.
Team \& Ecosystem Building
- Recruit, develop, and lead a high\-performing AI team: ML engineers, AI platform engineers, prompt engineers, and AI product specialists.
- Build a center\-of\-excellence model that distributes AI capability and fluency across business units.
- Manage strategic vendor and partner relationships — model providers, cloud AI services, and specialist consultancies.
WHAT YOU BRING
Required Experience
- 10\+ years in software engineering, data, or applied AI — with at least 3 years in a senior AI/ML leadership role.
- Deep hands\-on expertise with large language models (GPT\-4 class, Claude, Llama, Mistral) and small language models in production.
- Proven track record architecting and deploying agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or equivalent.
- Strong command of Model Context Protocols (MCPs) and tool\-use patterns for connecting agents to enterprise systems.
- Experience leading end\-to\-end AI\-powered business process automation — not just prototypes, but production\-grade systems with SLAs.
- Proficiency in Python and familiarity with cloud AI ecosystems (AWS Bedrock, Azure OpenAI, GCP Vertex AI).
- Demonstrated ability to translate ambiguous business problems into structured, solvable AI architectures.
- Must have used Claude Code, Codex or Github Copilot in the prior roles
Preferred Qualifications
- Experience with RAG pipelines, vector search, and knowledge graph integration at enterprise scale.
- Familiarity with AI governance standards, EU AI Act considerations, or SOC 2 / ISO 27001 in the context of AI systems.
- Background in enterprise architecture or integration patterns (event\-driven, microservices, API\-first).
- Published work, conference presentations, or open\-source contributions in AI/ML are a plus.
- \*\*\*\*\*Applicants must be authorized to work for any employer in the U.S. We are unable to sponsor or take over sponsorship of an employment visa at this time\*\*\*\*\*
Ryan Specialty is an Equal Opportunity Employer. We are committed to building and sustaining a diverse workforce throughout the organization. Our vision is an inclusive and equitable workplace where all employees are valued for and evaluated on their performance and contributions. Differences in race, creed, color, religious beliefs, physical or mental capabilities, gender identity or expression, sexual orientation, and many other characteristics bring together varied perspectives and add value to the service we provide our clients, trading partners, and communities. This policy extends to all aspects of our employment practices, including but not limited to, recruiting, hiring, discipline, firing, promoting, transferring, compensation, benefits, training, leaves of absence, and other terms, conditions, and benefits of employment.
How We Support Our Teammates
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Ryan Specialty seeks to offer our employees a comprehensive and best\-in\-class benefits package that helps them — and their family members — achieve their physical, financial, and emotional well\-being goals. In addition to paid time off for company holidays, vacation, sick and personal days, Ryan offers paid parental leave, mental health services and more.
The target salary range for this position is $168,000\.00 \- $210,000\.00 annually.
The wage range for this role considers many factors, such as training, transferable skills, work experience, licensure and certification, business needs, and market demands. The pay range is subject to change and may be modified in the future. Full\-time roles are eligible for bonuses and benefits. For additional information on Ryan Specialty Total Rewards, visit our website https://benefits.ryansg.com/.
We provide individuals with disabilities reasonable accommodations to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment in accordance with applicable law. Please contact us to request an accommodation at [email protected]
*The above is intended to describe this job's general requirements. It is not to be construed as an exhaustive statement of duties, responsibilities, or physical requirements. Nothing in this job description restricts management's right to assign or reassign duties and responsibilities to this job at any time. Reasonable accommodations may be made to enable individuals with disabilities to perform essential functions.*
Salary Context
This $168K-$210K range is above the median 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 Ryan Specialty Group, 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. Disclosed range: $168K to $210K.
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.
Ryan Specialty Group AI Hiring
Ryan Specialty Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Carmel, IN, US. Compensation range: $210K - $210K.
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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