Salesforce Agentforce Specialist

You will bridge the gap between business requirements and technical AI orchestration—leveraging Data Cloud, building precise prompts, and designing multi-agent environments while adhering to strict trust and security standards.
Technical Requirements
● Experience: 4+ years of hands-on Salesforce ecosystem experience (Admin/Developer/Consultant), with at least 1 year of dedicated experience building AI-driven CRM architectures (Agentforce or Einstein Copilot).
● Salesforce Core: Strong understanding of standard automation tools (Flows, Apex, Lightning Web Components) to support custom Agent actions.
● Data Literacy: Solid grasp of Salesforce Data Cloud, data modeling, ingestion, and vector search strategies.
● AI Core Concepts: Deep familiarity with prompt engineering guardrails, LLM reasoning logic, and token optimization.
Preferred Certifications
● Salesforce Certified Agentforce Specialist (AI-201) (Highly Preferred/Mandatory)
● Salesforce Certified AI Associate
● Salesforce Certified Platform App Builder or Platform Developer I (PD1)
● Salesforce Certified Data Cloud Consultant
Soft Skills
● Excellent communication skills with the ability to translate complex AI behavior into clear business logic for stakeholders.
● A strong analytical mindset to look through reasoning traces and continually tune AI responses.
Key Responsibilities
1. Agent Design & Orchestration
● Configure AI Agents: Design and deploy autonomous agents using Agent Studio utilizing Topics, Instructions, and Actions (TIA) to handle complex business scenarios.
● Action & Workflow Integration: Build and connect standard and custom agent actions by integrating Salesforce Flows, Apex actions, and external APIs.
● Multi-Agent Interoperability: Architect agent-to-agent communication protocols and utilize the Model Context Protocol (MCP) and Agent APIs for cross-platform workflows.
2. Prompt Engineering & Grounding
● Prompt Architecture: Author, manage, and optimize scalable prompt templates in Prompt Builder using field generation and flex types.
● Data Grounding: Implement robust grounding techniques using structured and unstructured business data to prevent AI hallucinations and ensure highly relevant agent responses.
3. Data Strategy & Data Cloud
● Data Library Management: Leverage the Agentforce Data Library to feed real-time context to the reasoning engine.
● Retrieval Optimization: Configure Data Cloud retrievers, data chunking, and indexing strategy across keyword, vector, and hybrid search methods.
4. Testing, Lifecycle & Security
● Validation: Use the Agentforce Testing Center and reasoning traces to evaluate, debug, and optimize agent decision intelligence and accuracy before live deployment.
● ALM & Deployment: Manage the application lifecycle of AI models, deploying configurations seamlessly from Sandboxes to Production environments.
● AI Governance: Enforce the Einstein Trust Layer, configure Agent Users secure access permissions, and ensure strict compliance with corporate data security and ethical AI practices.