Senior Salesforce / Agentforce, AI Platform Integration Architect

Role Overview

Team is seeking a highly skilled and strategic AI Platform Integration Architect to drive the next generation of our enterprise automation ecosystem. In this role, you will bridge the gap between our internal data architectures and Salesforce s advanced agentic capabilities. A core focus of this position is the evolution and enterprise scaling of an autonomous AI co-worker built and operated internally.

Must-Have Skills

Agentic AI Architecture

• Solid understanding of Large Language Model (LLM) orchestration, prompt engineering, context windows, and token conservation
• Experience designing autonomous routing systems that leverage semantic understanding to execute multi-step tool calls

Enterprise Integration

• Proven expertise in high-scale data synchronization patterns
• Masterful command of REST, gRPC, and event-driven architectures (e.g., Salesforce Pub/Sub API) to coordinate distributed cloud systems

Salesforce Customization

• Deep familiarity with wrapping complex backend processes into agentic tools via:
  • Invocable Apex methods
  • Advanced Salesforce Flows
  • Custom prompt templates

Architectural Governance

• Experience authoring comprehensive Architectural Decision Records (ADRs) that clearly map High-Level Requirements (HLRs) against technical constraints and explicit trade-offs

Must-Have Skills (Additional)

• Strong experience with enterprise integrations and system architecture
• Strong Apex methods, advanced Salesforce Flows, and custom prompt templates
• Experience with APIs (REST, gRPC, event-driven architectures)
• Experience integrating systems with Salesforce
• Knowledge of AI/LLM concepts such as:
  • Prompt engineering
  • Context management
  • Multi-step AI workflows
  • Agent orchestration
• Experience designing scalable and secure distributed systems
• Experience with Salesforce tools such as:
  • Salesforce Flows
  • APIs and connectors
• Understanding of security, authentication, and data governance
• Experience documenting architecture decisions and technical trade-offs
• Strong collaboration skills across engineering, data, and security teams

Nice-to-Have (Preferred)

• Experience with Model Context Protocol (MCP)
• Experience with Agent-to-Agent (A2A) integrations
• Experience with MuleSoft or middleware platforms
• Experience building or managing autonomous AI agents or micro-agent systems
• Familiarity with Salesforce Agentforce
• Experience with semantic tool discovery or AI-native integrations
• Knowledge of Bulk APIs and Salesforce Connect
• Experience working in large-scale enterprise AI environments

Key Responsibilities

System Orchestration & Blueprinting

• Architect end-to-end integration designs connecting Indeed s internal AI co-worker and related data platform services to Salesforce Agentforce environments

Pattern Optimization & Decisioning

• Apply rigorous architectural frameworks to choose the correct integration pattern (Traditional APIs, Model Context Protocol, or Agent-to-Agent) based on:
  • Transaction volume
  • Reasoning overhead
  • Latency boundaries
  • Data sensitivity

Agent Specialization Management

• Design modular agent structures to avoid monolithic, overloaded reasoning engines
• Enforce governance limits (e.g., maintaining under 10 topics per agent) by delegating complex tasks across specialized autonomous micro-agents

Cross-Functional Collaboration

• Work directly with Data Engineers, Salesforce Administrators, Core Platform Architects, and Security Operations
• Maintain unified API contracts and clean semantic understanding across all endpoints

Security and Trust Governance

• Establish explicit trust filters, secure authentication boundaries, and data exposure guardrails
• Protect sensitive corporate assets while maintaining fluid agent execution