Staff Enterprise AI Automation Engineer

Crusoe Crusoe · Data AI · San Francisco, CA - US · IT, Compliance, and Security

Staff Enterprise AI Automation Engineer to design and build agentic AI systems that move the organization from simple information retrieval to orchestrated, multi-system automation. Operates at the intersection of AI, enterprise systems, and integration platforms—building scalable agent workflows, enabling a citizen developer ecosystem, and establishing the technical foundations for an AI-powered operating model.

What you'd actually do

  1. Designing and implementing agentic AI workflows using a modular, API-first architecture across platforms such as Workato ONE, Anthropic Claude, and Gemini.
  2. Building autonomous agents that orchestrate workflows across enterprise systems (e.g., Salesforce, Coupa, Slack, Google Workspace)
  3. Architecting and integrating a unified data layer that enables AI agents to access and act on data across siloed systems
  4. Developing integrations, APIs, and custom connectors that enable scalable AI orchestration across business platforms
  5. Implementing MCP (Model Context Protocol) connectors and model-agnostic orchestration patterns

Skills

Required

  • Python
  • API development (REST, GraphQL, webhooks)
  • Enterprise integration platforms (e.g., Workato, MuleSoft, Zapier)
  • LLM APIs (OpenAI, Anthropic, Google Gemini, or similar)
  • Agentic architectures
  • RAG patterns
  • Prompt engineering
  • Scalable, distributed systems design
  • Cloud environments (AWS, GCP, or Azure)
  • Microservices
  • Event-driven architecture
  • Integration design patterns
  • CI/CD
  • Infrastructure as code
  • DevOps practices
  • Data security
  • Privacy
  • Compliance considerations (SOC 2, GDPR)

Nice to have

  • Experience deploying agentic AI systems in production environments
  • iPaaS platforms (Workato preferred)
  • Enterprise automation ecosystems
  • Google Workspace or Microsoft 365 automation and extensibility
  • Model Context Protocol (MCP) or similar interoperability standards
  • AI governance frameworks in enterprise settings
  • Infrastructure, energy, or high-performance computing environments
  • Contributions to open-source AI projects
  • Technical thought leadership

What the JD emphasized

  • 10+ years of software engineering experience, including 3+ years in AI/ML or AI application development
  • Strong proficiency in Python and API development (REST, GraphQL, webhooks)
  • Hands-on experience with enterprise integration platforms (e.g., Workato, MuleSoft, Zapier)
  • Experience working with LLM APIs (OpenAI, Anthropic, Google Gemini, or similar)
  • Deep understanding of agentic architectures, RAG patterns, and prompt engineering
  • Experience designing scalable, distributed systems in cloud environments (AWS, GCP, or Azure)
  • Strong knowledge of microservices, event-driven architecture, and integration design patterns
  • Experience with CI/CD, infrastructure as code, and DevOps practices
  • Understanding of data security, privacy, and compliance considerations (SOC 2, GDPR)

Other signals

  • designing and implementing agentic AI workflows
  • building autonomous agents that orchestrate workflows across enterprise systems
  • architecting and integrating a unified data layer that enables AI agents to access and act on data across siloed systems
  • developing integrations, APIs, and custom connectors that enable scalable AI orchestration across business platforms
  • embedding security, data privacy, and compliance guardrails into all AI implementations