You will build the agent platform that powers Verdi Assistant, a tool used by semiconductor engineers at some of the most advanced technology companies in the world.
Design and implement the Master Agent and Sub-Agent framework that orchestrates multi-step workflows, tool execution, and context handling across Verdi Assistant.
Build and maintain MCP Server integrations, including tool and resource interfaces, authentication flows, and runtime reliability mechanisms.
Develop backend services and orchestration logic in Python and C++ that power assistant workflows from planning through execution.
Integrate LLM-powered capabilities such as planning, tool-calling, context management, and retry logic into production-grade agent pipelines.
Define, develop, and maintain agent skills, including skill specifications, prompt engineering, tool bindings, and execution constraints that improve task quality and reusability.
Improve observability, debugging tools, and performance profiling for agent execution pipelines so engineers can diagnose issues quickly.
Collaborate directly with application engineers to deliver features to customers with short time-to-market, balancing speed with quality.
Shape the technical foundation of Verdi Assistant's next-generation agent platform, defining how thousands of engineers interact with AI-assisted workflows.
Increase assistant capability and reliability through robust Master and Sub-Agent orchestration that handles real-world complexity, not just happy-path scenarios.
Enable scalable integrations through MCP Server infrastructure that other teams and tools can build on top of.
Accelerate productivity for semiconductor engineers at companies like the Mag 7 and beyond by delivering practical AI-assisted workflows that actually save time.
Influence how Verdi Assistant evolves as a product, contributing architectural decisions that will shape the platform for years.
Build a system that runs in production across some of the most demanding engineering environments in the world, where reliability and performance are non-negotiable.
Help define what good agentic AI engineering looks like at Synopsys, setting patterns and practices that other teams will follow.
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