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Design, configure, and deploy multi-step AI agents using Salesforce Agentforce and Databricks platforms. Integrate enterprise data sources and implement security, observability, and evaluation frameworks for production workflows.
This is a remote position.
We are looking for two Agentic AI Engineers to design, build, and deploy autonomous and human-in-the-loop AI agents across our enterprise platforms. You will combine Salesforce Agentforce orchestration with Databricks data and machine learning capabilities to automate complex business workflows safely and reliably.
Key Responsibilities:
Design, configure, and deploy multi-step AI agents using Salesforce Agentforce (topics, actions, prompt templates, reasoning engines, and guardrails).
Build backend data, RAG, and tool-calling pipelines on Databricks (Mosaic AI, Vector Search, Unity Catalog, Model Serving).
Integrate enterprise APIs, knowledge graphs, and CRM/ERP data sources so agents can retrieve context and execute actions securely.
Implement evaluation frameworks, latency/cost optimizations, and observability for agentic workflows in production.
Enforce security, role-based access control (RBAC), and compliance guardrails across all agent interactions.
Work Schedule: During the onboarding phase, working hours may be adjusted to overlap with US hours until 7:00 PM at the latest.
3+ years of software engineering, ML engineering, or applied AI experience, with hands-on delivery of LLM-based agents in production.
Practical experience with Salesforce Agentforce (or Einstein Copilot / Salesforce AI ecosystem) and Databricks (Mosaic AI, Spark, Python, SQL).
Strong understanding of agentic architectures: tool/function calling, planning loops, memory management, and hybrid RAG.
Solid experience with REST/GraphQL APIs, authentication protocols (e.g., OAuth, OIDC), and enterprise data governance.
Ability to translate business workflows into deterministic guardrails and reliable prompt/action designs.
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