Salesforce Agentforce Developer

Role: Salesforce Agentforce Developer Pan India Experience – 7 to 14 years Role Title: AgentForce Developer Department: Engineering Role Overview: The AgentForce Engineer will be responsible for designing, developing, and scaling intelligent agent capabilities powered by structured and unstructured data, advanced language models, and enterprise-grade data platforms. The role requires strong expertise in Salesforce Agentforce, LLMs, prompt engineering, and Salesforce Data Cloud, along with the ability to build secure, compliant, and high-performing AI-driven solutions integrated across enterprise ecosystems.

Key Responsibilities
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Skills:
Core Skills &

Experience:
• Hands-on experience with Salesforce Agentforce and conversational AI frameworks.
• Strong understanding of prompt engineering, reasoning engines, and orchestration layers.
• Experience with Salesforce Data Cloud, including data ingestion, harmonization, identity resolution, and activation.
• Familiarity with observability tools and performance monitoring for AI systems.
• Experience integrating third-party platforms and enterprise data systems via APIs.
• Ability to analyze data insights and translate them into actionable improvements.
• Strong communication and collaboration skills across cross-functional teams. Key Success Criteria:
• Proven expertise in Salesforce LLMs, prompt engineering, and agent orchestration.
• Hands-on experience with Apex, APIs, and scalable integration patterns.
• Strong understanding of Data Cloud architecture, data modeling, and real-time data activation.
• Knowledge of secure data practices, compliance frameworks, and ethical AI principles.
• Ability to influence cross-functional decisions and contribute to architecture strategy.
• Experience with test automation frameworks (e.g., RAGAS, DeepEval) and QE collaboration. Data & Knowledge Engineering:
• Work with structured and unstructured data across enterprise systems and Salesforce Data Cloud.
• Design and manage data pipelines including ingestion, transformation, and harmonization.
• Integrate knowledge articles, knowledge graphs, vector databases, and unified customer profiles.
• Enable secure data access with robust data governance, masking, and PII protection mechanisms.
• Leverage Data Cloud for real-time data activation to power intelligent agent responses. Agent & Prompt Development:
• Design and optimize execution flows, prompts, and agent actions using Apex and APIs.
• Utilize Agent Builder and Prompt Builder to create scalable, reusable agent solutions.
• Collaborate on modeling strategies leveraging Salesforce LLMs, foundation models, and domain-specific models.
• Integrate Data Cloud insights into agent workflows for contextual and personalized interactions.
• Deploy and manage LLMs on cloud platforms such as Databricks and OpenAI. Trust, Compliance & Security:
• Ensure adherence to enterprise compliance standards and regulatory requirements.
• Implement monitoring mechanisms for toxicity, bias detection, and auditability.
• Maintain secure data handling practices across Agentforce and Data Cloud environments.
• Enforce authentication, authorization, and MFA standards across systems. Collaboration & Influence:
• Partner with Lead Engineers, Solution Architects, and Integration Architects.
• Influence non-functional requirements (NFRs) including scalability, performance, and security.
• Contribute to technical solution design documents (TSDs) and roadmap planning.
• Support QE teams by enabling automation-first testing strategies. Enablement & Lifecycle Management:
• Follow the Agentforce lifecycle: Ideate → Setup → Configure → Test → Deploy → Monitor.
• Contribute to internal knowledge sharing, documentation, and onboarding initiatives.
• Continuously improve implementation practices and contribute to the Agentforce playbook.