AI Builder Partner Solutions

Salesforce Salesforce · Enterprise · San Francisco, Illinois - Chicago, Massachusetts - Boston, New York - New York, Washington - Seattle, CA

Salesforce is seeking an AI Builder Partner Solutions role focused on building and implementing AI-powered solutions for customer and partner environments. This hands-on role involves creating technical solution patterns, POCs, and reusable accelerators, with a strong emphasis on agentic systems, tool use, retrieval, and evaluation. The position requires practical experience with LLM applications and agentic coding tools, as well as Salesforce platform development.

What you'd actually do

  1. Implement working Agentforce/Salesforce solution artifacts for the partner teams to use with the customer during qualification, shaping, and decision phases.
  2. Build with production-mindset for implementations that handle enterprise requirements such as identity, permissions, governed actions, observability, retrieval quality, and evaluation coverage.
  3. Create reusable accelerators that travel beyond the engagement: packaged metadata, deployment scripts, install guides, sample data, and test cases.
  4. Pair directly with partner engineers and customer technical teams to debug, refactor, and harden the implementation in proof-of-concepts & pilots.
  5. Produce clear walkthrough artifacts that increase Agentforce credibility and reuse: READMEs, partner delivery kits, technical posts, and recorded implementation walkthroughs.

Skills

Required

  • Salesforce platform development (Agentforce, Flow, Apex, LWC, APIs, permissions, metadata, packaging, deployment)
  • LLM application development (prompt design, context engineering, RAG, tool calling, evaluation, agent debugging)
  • Agentic coding tools (e.g., Claude Code, Cursor, GitHub Copilot)
  • Building and deploying enterprise-grade solutions
  • Creating reusable technical assets and documentation
  • Collaborating with partner and customer technical teams

Nice to have

  • Forward deployed engineering
  • Applied AI engineering
  • Solutions engineering
  • Technical consulting
  • Partner engineering
  • Technical founding

What the JD emphasized

  • Hands-on experience building on Salesforce platform
  • Practical experience building LLM-powered applications
  • Hands-on experience with agentic coding tools as a core part of your daily engineering workflow

Other signals

  • building working solutions against real customer challenges
  • hands-on builder role
  • create technical solution patterns and POCs
  • agents, on surfaces, using actions/integrations/context
  • implementing working Agentforce/Salesforce solution artifacts
  • handling enterprise requirements such as identity, permissions, governed actions, observability, retrieval quality, and evaluation coverage
  • building reusable accelerators
  • pair directly with partner engineers and customer technical teams to debug, refactor, and harden the implementation
  • produce clear walkthrough artifacts
  • present working implementations to experienced engineering audiences
  • deliver live technical sessions or co-builds
  • make ambiguous technical problems feel tractable through clarity and hands-on proof
  • build reusable assets that earn trust
  • help Salesforce earn relevance in AI and data conversations
  • represent Salesforce engineering credibility
  • turn repeatable field patterns into accelerators, solution assets, and Partner FDE Network resources
  • surface product friction, missing capabilities, and field-proven workarounds back to Agentforce, Data Cloud, MuleSoft, and Slack product and engineering teams
  • shipped at least one working Agentforce implementation inside an active sales cycle
  • built or contributed to a reusable accelerator, repo, or solution kit
  • created a written or recorded explainer of a real implementation
  • earned trust with at least one strong partner technical team
  • delivered at least one live technical session or co-build
  • influenced at least one strategic partner technical team's belief about Salesforce in the AI and data lane
  • shipped a body of working implementations, reusable assets, and technical content
  • produced evidence of impact through partner reuse, reduced implementation time, active production agents, or measurable contribution to qualified opportunity progression
  • practical experience building LLM-powered applications
  • prompt design, context engineering, retrieval and RAG, tool calling and actions, evaluation, and debugging agent behavior
  • hands-on experience with agentic coding tools as a core part of your daily engineering workflow