Principal Data and AI Architect

Salesforce Salesforce · Enterprise · Sao Paulo, Brazil

This role is for a Principal Data and AI Architect at Salesforce, focusing on designing and operationalizing enterprise-grade data and AI architectures centered on Salesforce D360 and the Agentforce platform. The role involves leading pre-sales technical design, shaping best practices for generative AI and agent interoperability, building prototypes, and acting as a technical advisor to customers and internal teams. It requires expertise in AI/ML concepts, prompt engineering, agent lifecycle management, and integrating with external agent ecosystems and data platforms.

What you'd actually do

  1. 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.
  2. 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.
  3. Drive Prompt Engineering & Lifecycle Strategy - Lead prompt design, testing, monitoring, and iteration; define agent lifecycle best practices from development through refinement.
  4. Build Hands-On Demos & Prototypes - Co-create quick prototypes (<2 weeks) with AEs demonstrating integration between Agentforce and external agents or services.
  5. Lead Pre-Sales Workshops - Facilitate whiteboarding, deep-dive sessions, and quick enablement for customers and internal teams.

Skills

Required

  • Technical Pre-Sales/Consulting - 7+ years of hands-on experience designing and delivering data, analytics, and AI architectures in enterprise environments.
  • Salesforce Expertise - Hands-on experience with Salesforce Agentforce and deep fluency with D360 and core Salesforce platform services.
  • External Ecosystem Knowledge - Strong understanding of external agent ecosystems and interoperability.
  • Proven Track Record - Experience 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.
  • Data Stack Knowledge - Deep expertise in modern cloud data platforms (Snowflake, Databricks, BigQuery, Redshift) and data ingestion patterns (batch, streaming, CDC).
  • Hands-On Development - Proficiency in programming (e.g., JavaScript, Python, SQL, R) and data frameworks like pa

What the JD emphasized

  • hands-on technical leader
  • hands-on prototypes
  • hands-on experience
  • Hands-On Development

Other signals

  • Designing and implementing enterprise-grade data and AI architectures
  • Guiding customers through emerging AI solutions
  • Integrating external agent frameworks and hyperscaler agents