AI Architect - Software Engineering

Salesforce Salesforce · Enterprise · Palo Alto, Washington - Seattle, California - San Francisco, CA

Salesforce is seeking an AI Architect to shape the development, deployment, and scaling of agentic systems within their Agentforce platform. This role involves inventing new primitives, incubating next-generation agentic systems, collaborating with customers, and defining architectural direction for enterprise AI agents in production. The position requires a blend of coding, whitepaper writing, runtime design, and executive communication.

What you'd actually do

  1. Drive fundamental advances in agentic AI — new execution models, agent orchestration frameworks, and stateful runtime architectures.
  2. Lead incubation projects exploring next-generation agentic systems for the enterprise, including OpenClaw-style reasoning architectures and emerging multi-agent coordination patterns.
  3. Influence core architectural decisions across Agentforce — agent determinism, A2A protocols, long-running async workflows, and context engineering.
  4. Contribute adjacently to the agent development lifecycle — including Agent Builder, Agent Testing Center, and Agent Observability — helping define how developers build, validate, and monitor agents end-to-end.
  5. Produce technically rigorous, persuasive content — design docs, engineering blog posts, developer documentation, and external presentations.

Skills

Required

  • Python
  • Java
  • distributed systems
  • stateful runtimes
  • LLM-based agent architectures
  • orchestration frameworks
  • graph-based execution engines
  • agent DSLs
  • LLM behavior
  • context management
  • agent reliability
  • cloud-native infra
  • frontier agentic research
  • reasoning loops
  • tool use
  • memory architectures
  • multi-agent systems
  • technical writing
  • customer engagement

Nice to have

  • Open-source community experience
  • developer advocacy
  • Salesforce platform primitives (Flow, Apex, Einstein, metadata)
  • ML infrastructure
  • data engineering
  • AI platform work
  • language/compiler design
  • observability tooling
  • testing frameworks
  • developer experience platforms

What the JD emphasized

  • Expert in distributed systems, stateful runtimes, and LLM-based agent architectures
  • Experience building orchestration frameworks, graph-based execution engines, or agent DSLs
  • Strong grasp of LLM behavior, context management, and agent reliability in production
  • You've shipped durable technical artifacts — platforms, frameworks, or open-source projects others build on
  • Exceptional written communication — from design docs to customer whitepapers

Other signals

  • AI CRM
  • Agentforce
  • agentic systems
  • enterprise AI agents