Principal Architect, Software Engineering — Sales Cloud

Salesforce Salesforce · Enterprise · San Francisco, CA

Salesforce is seeking a Principal Architect, Software Engineering for their Sales Cloud team. This role is a hands-on builder-architect responsible for end-to-end architecture of large-scale distributed software platforms, focusing on AI-native capabilities, agentic workflow automation, and integrating intelligent services. The role involves defining long-range plans, designing API-first and headless systems, implementing security patterns, and mentoring other engineers. A strong background in SaaS platforms, distributed systems, cloud-native infrastructure, and proven experience with AI tools and agentic workflows are required.

What you'd actually do

  1. Navigate technical and organizational complexity to drive cross-functional initiatives to completion; evaluate build vs. buy decisions and emerging platform capabilities (Agentforce, MCP, LLM APIs) with rigor and commercial awareness.
  2. Design and build large-scale distributed software platforms; writing production-quality code for core services, APIs, platform components, and reference implementations across architecture, infrastructure, security, and runtime.
  3. Design API-first, headless, and AI-native platform capabilities including secure tool invocation, model integration, context management, and agentic workflow automation.
  4. Implement and enforce security patterns for identity, authorization, API access, tenant isolation, and auditability; contribute to engineering-wide standards for API design, performance, and developer experience.
  5. Partner with other Principal Architects and senior engineers as a force multiplier: elevating technical quality through mentorship, design reviews, and thought leadership; own incident accountability for your domain.

Skills

Required

  • 12+ years of software engineering experience
  • hands-on architect on large-scale, multi-tenant SaaS platforms
  • Deep expertise in distributed systems
  • cloud-native infrastructure (Kubernetes, containers, CI/CD, observability)
  • API design
  • database optimization
  • enterprise security and integration patterns
  • Strong server-side engineering skills in Java
  • Solid foundation in web technologies (HTML, CSS, and JavaScript)
  • Proven track record of using AI tools to accelerate software development
  • advanced prompt engineering skills
  • experience maintaining structured system context that keeps agentic workflows accurate and on mission
  • Proven ability to design and build AI-native product capabilities
  • deep familiarity with Agentforce, LLMs, MCP, agentic orchestration, RAG, vector databases, and integrating intelligent services into production software
  • Deep knowledge of CRM and enterprise B2B SaaS software
  • Demonstrated ability to lead technical strategy across multiple engineering teams
  • Excellent written and verbal communication skills

Nice to have

  • Familiarity with Salesforce platform architecture, including multi-tenancy, governor limits, security models, and Sales Cloud APIs (REST, Bulk, Streaming, Platform Events)
  • experience with Data Cloud, Agentforce, and Hyperforce infrastructure

What the JD emphasized

  • hands-on, high-output software engineer
  • personally write the software required to make complex architectures real
  • Proven track record of using AI tools to accelerate software development
  • hands-on fluency with tools like Claude, Cursor, or equivalent
  • advanced prompt engineering skills
  • experience maintaining structured system context that keeps agentic workflows accurate and on mission
  • Proven ability to design and build AI-native product capabilities
  • deep familiarity with Agentforce, LLMs, MCP, agentic orchestration, RAG, vector databases, and integrating intelligent services into production software
  • Deep knowledge of CRM and enterprise B2B SaaS software
  • candidates with exclusively B2C backgrounds are not a fit

Other signals

  • AI-native selling experiences powered by Agentforce
  • AI-native platform capabilities
  • Agentforce, LLMs, MCP, agentic orchestration, RAG, vector databases
  • AI-accelerated engineering practices