MODULE LEAD - Salesforce

Job Title: Senior Salesforce Agentforce Developer

Experience Level: Mid - Senior (5+ Years Salesforce Dev | 1+ Year Agentforce / Generative AI)

Role Overview
We are seeking a Salesforce Agentforce Developer to design, build, and deploy autonomous AI agents across our Salesforce ecosystem. In this role, you will leverage Agent Studio, Prompt Builder, Data Cloud, and the Einstein Trust Layer to create intelligent, action-oriented agents that handle complex customer and employee workflows natively within Salesforce.

Key Responsibilities
Agent Design & Configuration: Build and fine-tune autonomous AI agents in Agentforce / Agent Studio, defining explicit Topics, Instructions, Guardrails, and Action triggers.

Custom Action Development: Develop custom Apex (Invocable Methods), Auto-launched Flows, and API callouts to serve as execution actions for Agentforce reasoning engines.

Prompt Engineering & Tuning: Craft, test, and optimize grounded prompts in Prompt Builder to maximize agent accuracy, context retention, and reasoning capabilities.

Data Cloud & RAG Grounding: Integrate Salesforce Data Cloud data models, vector databases, and Unstructured Data indexing to ground agents in real-time enterprise context.

Trust & Security Governance: Configure the Einstein Trust Layer, setting up dynamic PII masking, toxicity filtering, zero-data-retention rules, and human-in-the-loop (HITL) handoffs.

Testing & Analytics: Monitor agent performance using Agentforce Analytics to track intent accuracy, topic drift, resolution rates, and guardrail enforcement.

Required Technical Skills
Agentforce & AI Stack: Hands-on experience with Salesforce Agentforce (Agent Studio), Prompt Builder, Einstein Copilot, and the Einstein Trust Layer.

Core Salesforce Development: Advanced proficiency in Apex (Invocable Apex, Trigger frameworks, Asynchronous Apex), LWC (Lightning Web Components), and Flow Builder.

Data Integration: Strong background with Data Cloud, REST/SOAP APIs, Named Credentials, and MuleSoft/middleware integrations.

AI & Prompting Mechanics: Understanding of Retrieval-Augmented Generation (RAG), LLM reasoning loops, parameter passing, and natural language instruction design.