Director of Enterprise AI

About Boomi and What Makes Us Special

Are you ready to work at a fast-growing company where you can make a difference?

Boomi, the data activation company for AI, powers the agentic enterprise by bringing data to life across the business. The award-winning

Boomi Enterprise Platform is the active data foundation that delivers essential agentic infrastructure enabling organizations to drive agentic transformation and harness the power of AI with secure, scalable connectivity.

Trusted by over 30,000 customers and supported by a network of 800+ partners, Boomi helps organisations of all sizes achieve agility, efficiency, and innovation  Boomi, you’ll work with world-class people and industry-leading technology. We hire trailblazers with an entrepreneurial spirit who can solve challenging problems, make a real impact, and want to be part of building something big. If this sounds like a good fit for you, check  or visit our Boomi Careers page to learn more.

Location: US national remote

Reports to: Vice President, Enterprise AI

Boomi is becoming an AI-first enterprise, and this role leads both halves of that shift: the enterprise AI strategy and the data foundation that grounds it. As Director, Enterprise AI, you will be responsible for leading the team that agentifies the enterprise. You will also build the knowledge graph, semantic layer, retrieval, and trust layer every agent depends on. You will report directly to the VP of Enterprise AI.

• 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.

• 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.
• Decide when to self-host open-weight models (via vLLM, Ollama or managed endpoints) and when to use hosted APIs, and run the evaluation that backs each choice.

• Define and evolve the reference architecture across the knowledge graph, semantic layer, vector retrieval, and hosted trust layer that grounds every AI agent.
• Own the trust layer charter: classification, masking, lineage, and policy enforcement for data that lacks native role-based access control.
• Lead batch and real-time pipelines that move source data into AI-ready form for models and agents.
• Set data modeling standards, canonical entities, and shared…