Director of Enterprise AI

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 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, backed by data quality checks, drift monitoring, and lineage so every agent action traces to its data.
• Balance performance and cost across storage, query, and compute for training, retrieval, and inference.

• Mentor, and grow a team of AI and data engineering professionals as a player-coach, with a culture of exploration and excellence.
• Lead async, multi-geo 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 ahead of AI trends, and work with Boomi Product & Engineering and external communities to test ideas and sharpen the AI strategy.

• 10+ years across data engineering, analytics, and AI systems, including 3+ years managing senior IC engineers.
• A 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 on quality and total cost.
• Experience designing or operating an AI gateway or LLM proxy layer (such as LiteLLM, Portkey, Kong AI Gateway, Cloudflare AI Gateway or AWS Bedrock) for multi-model routing, observability and governance.
• Has personally architected at least one of: a production knowledge graph, a semantic layer, or a RAG retrieval system at enterprise scale, and stays close to the code as a player-coach.
• Experience setting architecture for agentic or multi-agent AI systems, including guardrails, evaluation, and observability.
• Has built a data trust or governance layer for data without native RBAC: classification, masking, lineage, and policy enforcement.
• Hands-on AWS depth: IAM, S3, Glue/EMR, Lambda, Bedrock or equivalent, OpenSearch, KMS.
• Model access: Anthropic Claude, OpenAI, Google Gemini; open-weight models (Llama, Mistral, Qwen, DeepSeek) via vLLM, Ollama or managed endpoints
• AI gateways and routing: LiteLLM, Portkey, Kong AI Gateway, Lavendr, Cloudflare AI Gateway, 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: explains complex AI concepts to executives, customers, and diverse stakeholders.
• BS or advanced degree in engineering, computer science, or another technical field.

• 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.

• Languages: Python, TypeScript, SQL
• Data platforms: Snowflake, Redshift
• Ingestion and streaming: Kafka, Kinesis
• Transformation and orchestration: Airflow
• Model access: Anthropic, OpenAI, Google, open-weight models via vLLM or Ollama
• Retrieval: Embeddings, pgvector, Pinecone, Qdrant, hybrid search
• AI frameworks: LangChain, LlamaIndex, PyTorch, Hugging Face, Lavendr
• Evaluation: Ragas, DeepEval, Promptfoo, golden datasets
• LLMOps and observability: LangSmith, Langfuse, Arize Phoenix
• Agent frameworks: LangGraph, CrewAI, OpenAI Agents SDK, Claude Agent SDK
• Tool standards: Model Context Protocol (MCP), function calling
• Agent state and durability: Temporal, Restate
• Guardrails: Open Policy Agent, PII detection, prompt injection defenses, human-in-the-loop approvals
• Infrastructure: Kubernetes, Terraform, Docker, GitHub Actions
• Cloud: AWS (primary), with Azure or GCP a plus
• Security and monitoring: OAuth/OIDC, Vault, OpenTelemetry, Grafana