Boomilp Director of Enterprise AI leads AI strategy and data foundation to transform Boomi into an AI-first enterprise. Responsible for AI adoption, agentic AI solutions, and managing frontier and open-weight model strategies.
Responsibilities
Enterprise AI strategy and transformation
Develop and implement the AI strategy and blueprint that turns Boomi into an AI-first enterprise.
Lead AI adoption across internal business operations, aligned with the corporate vision.
Run quarterly planning with the VP of AI and business teams, and translate the AI and agent roadmap into agent, data, and platform investments.
Agentic AI and AI solutions
Drive AI solutions that improve front- and back-office processes, decision-making, and customer experiences, using LLMs, RAG, machine learning, NLP, and computer vision.
Set the architecture and roadmap for autonomous and multi-agent systems: planning, tool use, memory, orchestration, and integration with enterprise APIs, and workflows.
Define guardrails for agent behavior, including least-privilege access, human-in-the-loop checkpoints, action logging, and escalation paths.
Establish evaluation and observability standards for agents: task success, safety, reliability, cost per task, and regression testing before release.
Drive interoperability standards such as MCP and open agent-to-agent protocols, and own the agent operating model: ownership, lifecycle, and decommissioning.
Own the model strategy across frontier models such as Anthropic’s Claude, OpenAI and Google Gemini, and open-weight models such as Llama, Mistral and Qwen, matching each workload to the right model on quality, cost, latency and data-residency needs.
Stand up and govern an enterprise AI gateway that centralizes model access, routing, fallback, rate limiting, cost tracking and policy enforcement across every team and agent.
Qualification
Familiarity with dbt Semantic LayerLanguagesData platformsIngestion and streaming
Preferred
Experience supporting sales or revenue agents, or CRM-grounded AI applications.
Familiarity with dbt Semantic Layer, Cube, AtScale, or comparable metric platforms.
Prior experience at an integration platform, iPaaS, or data fabric company.
Open-source contributions in data or AI infrastructure (dbt, Neo4j, a vector DB, agent frameworks).
A public voice in practical enterprise AI through talks, writing, or community work.
