You will bridge the gap between business requirements and technical AI orchestration-leveraging Data Cloud, building accurate 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.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Salesforce Agentforce Specialist (Chennai)
🏢 Zensar
📍 Chennai