Director, AI and Agentic Engineering

Ryan Specialty•Carmel, IN

5d•$168,000 - $210,000

About The Position

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.

Requirements

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
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.

Nice To Haves

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.

Responsibilities

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.
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.
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.
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.