Salesforce

Principal AI Architect

Salesforce

full-time

Posted on:

Origin:  • 🇺🇸 United States • New York

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Salary

💰 $143,850 - $287,210 per year

Job Level

Lead

Tech Stack

AWSCloudJavaScriptPythonPyTorchSFDCSQLTensorflow

About the role

  • Lead pre-sales technical design by analyzing customer needs and recommending solutions aligned with Agentforce capabilities and integration with external agent frameworks.
  • Shape best practices around generative AI, agent interoperability, prompt engineering, Data Cloud, and cross-platform integrations.
  • Collaborate with AEs and SEs to build hands-on prototypes and demos using Agentforce and integrated external agents.
  • Develop thought leadership content—demo templates, whitepapers, enablement sessions—focused on agent lifecycle, integration strategy, and technical effectiveness
  • Act as a central technical knowledge resource, proactively addressing internal technical inquiries, facilitating deep technical enablement, and documenting best practices to empower specialist teams across the organization.
  • If you are naturally curious about AI, love diving into new technologies, and enjoy educating others while crafting solutions that deliver real business impact, we want to talk to you!
  • Responsibilities
  • Understand Agent Interoperability - Map and integrate external agents from hyperscalers (e.g. Copilot, Gemini) into Agentforce via open standards (MCP, A2A); design how these systems collaborate.
  • Enable Conversational & Background Agents - Use Agentforce Studio and Agent Builder to configure chat and background agents; integrate with external channels including voice via hyperscaler APIs.
  • Drive Prompt Engineering & Lifecycle Strategy - Lead prompt design, testing, monitoring, and iteration; define agent lifecycle best practices from development through refinement.
  • Build Hands-On Demos & Prototypes - Co-create quick prototypes (<2 weeks) with AEs demonstrating integration between Agentforce and external agents or services.
  • Lead Pre-Sales Workshops - Facilitate whiteboarding, deep-dive sessions, and quick enablement for customers and internal teams.
  • Advise on Data & Integration- Integrate Data Cloud, CRM, MuleSoft APIs, and external agent endpoints ensuring cohesive architectures that align with compliance and governance policies.
  • Support Early Adoption - Occasionally assist in proof-of-value engagements post-sale by tuning agents and guiding customers toward self-sufficient enablement.
  • Own Technical Enablement: Create and manage accessible technical documentation, knowledge bases, and FAQ resources to rapidly resolve internal technical inquiries, empowering specialist teams to handle technical discussions confidently.

Requirements

  • Technical Pre-Sales/Consulting: Several years in solutions engineering, architecture, or technical consulting, ideally in B2B SaaS.
  • Hands-on experience with Salesforce Agentforce
  • Strong understanding of external agent ecosystems and interoperability
  • Proven track record in prompt engineering, agent lifecycle management, and hands-on prototype development.
  • AI & ML Expertise: Experience with machine learning concepts (predictive and generative AI), plus the ability to communicate value to diverse audiences.
  • CRM & Data Knowledge: Familiarity with Salesforce CRM and modern data stacks; comfortable discussing governance, security, and integration.
  • Hands-On Development: Proficiency in programming (e.g., JavaScript, Python, SQL) or Salesforce development (Apex, Lightning Web Components, etc.).
  • Excellent Communication: Strong presentation skills; adept at explaining complex ideas and guiding stakeholders toward impactful solutions.
  • Curiosity & Continuous Learning: Passion for exploring new AI frameworks, sharing insights, and experimenting with cutting-edge technologies.
  • Experience integrating Salesforce with external agents via APIs and open standards (MCP, A2A).
  • Familiarity with prompt governance, observability, and monitoring frameworks.
  • Background in cross-platform integrations (e.g., Hyperscaler SDKs to Salesforce Flows).
  • Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services.
  • Advanced AI/ML: Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms (e.g., Google Vertex AI, AWS Sagemaker).
  • Hands-on work with Generative AI, Large Language Models (LLMs), agent-based frameworks, and prompt engineering.