Salesforce AI Integration Architect ID68517

AgileEngine•Houston, TX

1d•Hybrid

About The Position

AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards. We are looking for a Salesforce AI Integration Architect to design and build enterprise integrations between internal AI platforms and Salesforce Agentforce environments, scaling autonomous AI workflows across distributed systems. You will architect multi-step agentic AI orchestration patterns, define integration strategies using REST APIs, gRPC, and event-driven architectures, and establish security and governance guardrails for AI-native enterprise systems. The role requires deep expertise in Salesforce customization, LLM orchestration, and scalable distributed system design.

Requirements

Must be authorized to work for ANY employer in the US (e.g., Green card holders, TN visa holders, GC EAD, H4 EAD, U4U with EAD), as we are unable to sponsor or take over employment visa sponsorship at this time
6+ years of experience designing enterprise integrations and distributed system architectures
Hands-on experience integrating systems with Salesforce
Deep expertise with Apex methods, advanced Salesforce Flows, custom prompt templates, and Salesforce APIs and connectors
Strong experience with REST APIs, gRPC, event-driven architectures, and enterprise synchronization patterns
Solid understanding of AI and LLM concepts including prompt engineering, context management, token optimization, multi-step AI workflows, agent orchestration, and semantic routing systems
Experience designing scalable and secure distributed systems
Strong understanding of authentication, security, trust boundaries, and data governance
Experience documenting architecture decisions, trade-offs, and technical strategy
Excellent collaboration and communication skills across engineering, platform, data, and security organizations
Upper-intermediate English level

Nice To Haves

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

Responsibilities

Architect end-to-end integrations between internal AI platforms, enterprise data systems, and Salesforce Agentforce environments
Design scalable, secure, and resilient distributed system architectures supporting autonomous AI workflows
Define integration strategies leveraging REST APIs, gRPC, event-driven architectures, and Salesforce-native capabilities
Design and optimize modular agentic AI systems through specialized micro-agent delegation
Build orchestration patterns for multi-step AI workflows, autonomous routing systems, and semantic tool execution
Apply best practices around prompt engineering, context management, token conservation, and LLM orchestration
Develop and enhance integrations using Salesforce Flows, Invocable Apex methods, APIs, connectors, and custom prompt templates
Enable complex backend processes to be exposed as intelligent agentic tools within Salesforce ecosystems
Collaborate with cross-functional teams to maintain unified API contracts and semantic consistency across enterprise systems
Author architectural decision records documenting technical trade-offs, constraints, and high-level requirements
Evaluate and select integration patterns including traditional APIs, Model Context Protocol, and Agent-to-Agent communication models
Balance performance, latency, scalability, reasoning overhead, and data sensitivity considerations in architectural decisions
Establish authentication boundaries, trust layers, and governance guardrails for AI-enabled enterprise systems
Ensure compliance with enterprise security standards, data governance policies, and secure data exposure practices
Partner with security and platform teams to maintain reliable and trustworthy autonomous agent execution