Agentforce Engineer
| Company | Salesforce |
|---|---|
| Location | Seattle, WA, USA, Indianapolis, IN, USA, San Francisco, CA, USA, Chicago, IL, USA, New York, NY, USA, Bellevue, WA, USA, Atlanta, GA, USA |
| Salary | $133400 – $244200 |
| Type | Full-Time |
| Degrees | |
| Experience Level | Senior |
Requirements
- 5+ years experience with cloud based technologies and architecture principles
- Advanced or expert knowledge of Salesforce and Agentforce and AI
- Experienced with Data Cloud or similar data integration platforms
- Other Salesforce Certs such as Advanced Admin, Service Cloud, Data Cloud, Agentforce, or Sales Cloud
- Solid understanding of AI concepts, especially conversational AI or intelligent agents.
- Hands-on experience integrating Large Language Models (LLMs) or AI services into applications
- Experience with AI orchestration frameworks or prompt engineering
- Demonstrated ability to grasp and learn new business models and technology paradigms
- Cloud technologies and low code integration/automation solutions
- Consistent proactive thinking and the desire to self-start on learning and applying new technologies.
- Ability to design scalable solutions for complex business requirements.
- Skilled in debugging, optimizing, and ensuring the reliability of integrated AI-Salesforce solutions.
- Strong proficiency in Salesforce Flow, Lightning Web Components (LWC), Apex, Salesforce APIs, and Agent Studio
Responsibilities
- Assess technical and business readiness and map organizational processes through direct customer engagement
- Lead the end-to-end design, configuration, and deployment of Agentforce agents within Salesforce; building agents that meet compliance requirements, business logic, and customer experience goals
- Recommend opportunities to reengineer processes by combining AI agent capabilities with Salesforce automation tools such as Flows and OmniStudio.
- Identify and provide recommendations on data strategy, data quality, governance (e.g., legal reviews, AI council), and software development lifecycle best practices.
- Define and orchestrate agent actions, configure and maintain each agent’s topics, and create and optimize prompt engineering strategies to improve agent performance, relevance, and accuracy
- Build and maintain guardrails to govern agent behavior, ensuring safe, compliant, and brand-aligned responses, including risk mitigation, escalation paths, constraints, and tone-of-voice calibration
- Implement and configure leading large language models (LLMs) like GPT-4 for Salesforce agents
- Perform testing and refinement in customer environment throughout deployment to optimize agent performance
- Track performance metrics and user feedback; execute iterative improvements based on performance metrics to grow Agentforce consumption
- Implement feedback and collaborate actively with Product organization before, during, and after agent deployments; execute pilot programs and beta testing of new features
- Build and maintain knowledge bases and technical documentation for internal and customer use
- Develop and implement standardized agent deployment processes; create and maintain integration patterns across Salesforce platforms
- Serve as a key feedback conduit between customer implementations and the Salesforce TMP organization
- Deliver guidance on best practices to ensure customers can self-sustain their Agentforce implementation.
Preferred Qualifications
- Familiarity with building Einstein Bots or Agentforce agents is a major plus.
- Experience deploying conversational AI solutions within regulated or highly sensitive environments (e.g., healthcare, finance).