Director of Enterprise AI

Boomi

3h•$222,960 - $278,700•Remote

About The Position

Boomi is becoming an AI-first enterprise, and the Director of Enterprise AI leads both the enterprise AI strategy and the data foundation that supports it. The role leads the team responsible for agentifying the enterprise and builds the knowledge graph, semantic layer, retrieval, and trust layer on which AI agents depend. The position reports to the Vice President, Enterprise AI and is US national remote. The Director develops Boomi’s AI strategy and blueprint, leads adoption across internal operations, and works with the VP of Enterprise AI and business teams on quarterly planning and roadmap investments. The role sets architecture and operating standards for agentic systems, model access, AI gateways, evaluation, observability, and responsible agent behavior. It also defines the data architecture and governance foundations that ensure agents use reliable, appropriately protected, traceable data. As a player-coach, the Director mentors and grows AI and data engineering professionals, establishes collaboration norms across the US and India, and represents Boomi’s practical AI experience to customers and partners. The role works with Product & Engineering and external communities to test ideas and evolve the company’s AI strategy.

Requirements

10+ years of experience across data engineering, analytics, and AI systems, including 3+ years managing senior individual-contributor engineers.
Track record leading enterprise-scale AI initiatives that turned large business transformations into measurable outcomes.
Hands-on experience building production solutions on Claude, including the Claude API, Claude Agent SDK, tool use, and prompt caching, plus at least one other frontier model family.
Experience deploying, fine-tuning, or serving open-weight models in production and comparing them against hosted models for quality and total cost.
Experience designing or operating an AI gateway or LLM proxy layer for multi-model routing, observability, and governance.
Personally architected at least one production knowledge graph, semantic layer, or enterprise-scale RAG retrieval system, while staying close to the code as a player-coach.
Experience setting architecture for agentic or multi-agent AI systems, including guardrails, evaluation, and observability.
Experience building a data trust or governance layer for data without native RBAC, including classification, masking, lineage, and policy enforcement.
Hands-on AWS depth, including IAM, S3, Glue/EMR, Lambda, Bedrock or equivalent, OpenSearch, and KMS.
Experience with model access across Anthropic Claude, OpenAI, Google Gemini, and open-weight models such as Llama, Mistral, Qwen, or DeepSeek via vLLM, Ollama, or managed endpoints.
Experience with AI gateways and routing tools such as LiteLLM, Portkey, Kong AI Gateway, Lavendr, Cloudflare AI Gateway, or Amazon Bedrock.
Experience leading large transformations across strategic planning, program execution, technical delivery, communications, and change management.
Proven record building and mentoring AI and software engineering teams, including a distributed US-India team.
Strong executive communication skills, including explaining complex AI concepts to executives, customers, and diverse stakeholders.
BS or advanced degree in engineering, computer science, or another technical field.

Nice To Haves

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, such as dbt, Neo4j, a vector database, or agent frameworks.
A public voice in practical enterprise AI through talks, writing, or community work.
Familiarity with Azure or GCP is a plus.

Responsibilities

Develop and implement the AI strategy and blueprint for turning Boomi into an AI-first enterprise.
Lead AI adoption across internal business operations in alignment with the corporate vision.
Run quarterly planning with the VP of AI and business teams, translating 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 technologies including LLMs, RAG, machine learning, NLP, and computer vision.
Set the architecture and roadmap for autonomous and multi-agent systems, including planning, tool use, memory, orchestration, and integration with enterprise APIs and workflows.
Define agent behavior guardrails, including least-privilege access, human-in-the-loop checkpoints, action logging, and escalation paths.
Establish agent evaluation and observability standards covering 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, including ownership, lifecycle, and decommissioning.
Own model strategy across frontier and open-weight models, matching workloads to models based on quality, cost, latency, and data-residency needs.
Stand up and govern an enterprise AI gateway for model access, routing, fallback, rate limiting, cost tracking, and policy enforcement.
Decide when to self-host open-weight models and when to use hosted APIs, and run evaluations to support those decisions.
Define and evolve the reference architecture for the knowledge graph, semantic layer, vector retrieval, and hosted trust layer.
Own the trust layer charter, including classification, masking, lineage, and policy enforcement for data without native role-based access control.
Lead batch and real-time pipelines that prepare source data for AI models and agents.
Set data modeling standards, canonical entities, and shared business definitions, and build versioned feature and context stores with freshness, latency, and access SLAs.
Establish SLOs for retrieval quality, data freshness, and trust-layer policy compliance, supported by data quality checks, drift monitoring, and lineage.
Balance performance and cost across storage, query, and compute for training, retrieval, and inference.
Mentor and grow AI and data engineering professionals as a player-coach, fostering a culture of exploration and excellence.
Lead asynchronous, multi-geography collaboration norms across the US and India, and defend team scope against feature-factory pressure.
Serve as Customer Zero for Boomi’s low-code generative AI orchestration platform and share practical lessons with customers and partners.
Stay current with AI trends and work with Boomi Product & Engineering and external communities to test ideas and sharpen the AI strategy.