Principal Solution Engineer - AI and Data Cloud

Salesforce Salesforce · Enterprise · Bangalore, India

This role is for a Principal Solution Engineer focused on AI and Data Cloud at Salesforce. The primary responsibility is to act as a senior technical leader and trusted advisor to customers, designing and operationalizing enterprise-grade data and AI architectures centered on Salesforce D360 and the Agentforce platform. The role involves pre-sales technical design, shaping best practices for generative AI and agent interoperability, building prototypes, developing thought leadership content, and advising on data integration and architecture decisions. It requires deep expertise in Salesforce, external agent ecosystems, prompt engineering, and modern cloud data platforms.

What you'd actually do

  1. Lead pre-sales technical design by analyzing customer needs and recommending solutions aligned with Agentforce capabilities and integration with external agent frameworks.
  2. Shape best practices around generative AI, agent interoperability, prompt engineering, Data Cloud, and cross-platform integrations.
  3. Collaborate with AEs and SEs to build hands-on prototypes and demos using Agentforce and integrated external agents.
  4. Develop thought leadership content—demo templates, whitepapers, enablement sessions—focused on agent lifecycle, integration strategy, and technical effectiveness.
  5. 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.

Skills

Required

  • Technical Pre-Sales/Consulting - 10+ 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 pandas or Jupyter.
  • 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.

Nice to have

  • Advanced Integration - Experience integrating Salesforce with external agents via APIs and open standards (MCP, A2A).
  • Governance & Observability - Familiarity with prompt governance, observability, and monitoring frameworks.
  • Cross-Platform Background - Background in cross-platform integrations (e.g., Hyperscaler SDKs to Salesforce Flow

What the JD emphasized

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

Other signals

  • Designing, validating, and operationalizing enterprise-grade data and AI architectures
  • Guiding customer Chief Data Officers and Enterprise Architects through emerging AI solutions
  • Integrating D360 into complex enterprise ecosystems
  • 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
  • Develop thought leadership content—demo templates, whitepapers, enablement sessions—focused on agent lifecycle, integration strategy, and technical effectiveness
  • 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.
  • Advise on Data & Integration - Integrate Data Cloud (D360), CRM, MuleSoft APIs, and external agent endpoints ensuring cohesive architectures that align with compliance and governance policies.
  • Own Technical Architecture Decisions - Oversee data modeling, identity resolution, real-time vs. batch patterns, data graph design, and activation strategies within D360.