Application Programming Interface (API), Artificial Intelligence (AI), Artificial Intelligence (AI) Agents, Authentication, Automation, Cloud Computing, Conservation, Contract Management, Cross-Functional, Customer Support/Service, Distributed Computing, Documentation, Ecosystems, Modeling Languages, REST (Representational State Transfer), Security Architecture, Semantic Reasoner, System Architecture, System Integration (SI), Systems Administration/Management
Role Name: Client AI Platform Integration Architect _ Agentforce
Work site: Austin, US (Remote)
Key Capabilities:
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., Client Pub/Sub API) to coordinate distributed cloud systems
Client Customization
Deep familiarity with wrapping complex backend processes into agentic tools via:
Invocable Apex methods
Advanced Client 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 Client Flows, and custom prompt templates
Experience with APIs (REST, gRPC, event-driven architectures)
Experience integrating systems with Client
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 Client tools such as:
Client 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 (ClientP)
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 Client Agentforce
Experience with semantic tool discovery or AI-native integrations
Knowledge of Bulk APIs and Client Connect
Experience working in large-scale enterprise AI environments
Role Overview
Indeed’s GTM Core 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 Client’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.
Key Responsibilities
System Orchestration & Blueprinting
Architect end-to-end integration designs connecting Indeed’s internal AI co-worker and related data platform services to Client 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, Client 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
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