Manager – Customer Interaction Suite

Overview

As an AI OS Architect, you will shape an enterprise AI operating system by turning complex customer realities into reusable ontologies and governed data layers. You will embed with 1–2 large clients, delivering end-to-end architectures from discovery to production, and orchestrating LLM-driven agents within security and cost constraints. You drive reusable patterns across customers, influencing product direction through field learnings. This role blends hands-on engineering with strategic customer engagement to enable scalable, governed AI deployments.

Responsibilities

• Lead enterprise discovery and model enterprise objects, actions, and processes into a reusable ontology
• Architect data foundations with governed pipelines, lakehouse patterns, lineage, and access controls
• Design for reuse with permissioned, versioned, and composable objects and processes
• Develop AI OS layer including RAG, context engineering, agent orchestration, tool-calling, and human-in-the-loop mechanisms
• Ensure model governance, cost per action, prompts/version control, guardrails, bias checks, drift monitoring, and audit trails
• Be the technical lead in 1–2 customer engagements, conducting design workshops and delivering a production-ready reference architecture
• Prototype value quickly with small-scale deployments and iterate from field learnings into product roadmap
• Provide feedback to product and portfolio teams based on customer field experiences

Key requirements

• Enterprise AI platform deployment experience in forward-deployed or embedded settings (Palantir, Databricks, Snowflake, AWS/Azure/GCP AI stacks, or equivalent)
• Ontology-first modeling of enterprise objects, actions, and processes with a reusable semantic layer, including metadata, permissions, and secure data structures
• Deep data engineering experience with lakehouse/ data platform architectures, streaming and batch processing, data quality and lineage, zero-copy patterns
• Hands-on experience with LLM/agentic AI, RAG, context engineering, prompt/orchestrator tooling, and evaluation loops
• Strong governance and security knowledge (IAM, row/object-level permissions, model governance, SOC2/ISO27001/GDPR compliance)
• Forward-deployed temperament and strong communication skills to explain architecture to executives and travel as needed
• Excellent written documentation and client-facing communication

• Client-facing communication
• Collaboration
• Adaptability and comfort with ambiguity
• Ontology architecture
• Data pipeline design and data lakehouse
• SQL, PySpark or equivalent

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