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# Agentic AI Architect

- **Company:** Broadcom
- **Location:** Saint Paul, MN, United States
- **Experience:** Expert
- **Salary:** $129,400.0 - $207,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Board Bringup, Artificial Intelligence, Computer Programming, Computer Engineering, Information Engineering, Software Debugging, Electronic Design Automation, Hardware Design, Interoperability, Python (Programming Language), Software Architecture, Software Tools, Software Engineering, Systems Architecture, SystemVerilog, Systems Integration, Tcl (Programming Language), Large Language Models, Multi-Agent Systems, Generative AI, Agentic-AI, Kubernetes, Information Technology, Virtual Agents, Restful APIs, Model Context Protocol, Docker, Microservices
- **Published:** September 30, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3412433645&tx=KJ3939FFF&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

Experience: 10+ years of progressive, relevant experience in software architecture, AI systems

engineering, or semiconductor automation design.

? Education: Master's degree or Ph.D. in Electrical Engineering, Computer Engineering, Computer

Science, or a closely related technical field., ? Experience architecting production-grade LLM applications: multi-agent orchestration,

function-calling/tool-use, and retrieval-augmented generation (RAG).

? Experience integrating agentic systems with enterprise tools, services, and knowledge

sources - including building and consuming Model Context Protocol (MCP) servers, and

packaging reusable agent capabilities (e.g., Agent Skills).

? Deep experience with agent orchestration frameworks such as LangGraph.

? Semiconductor Domain Knowledge & EDA Tooling:

? Fundamental grasp of chip design methodologies, IP block development lifecycles, and

verification environments.

? Hands-on familiarity with industry-standard EDA tooling and frameworks

from Synopsys, Cadence, and Siemens EDA, alongside System Verilog &

UVM verification environments.

? Software Engineering & Infrastructure Mastery:

? High proficiency in core programming languages and system design concepts,

specifically Python and TCL.

? Expertise in microservices architecture, REST APIs, Docker, Kubernetes, Sandboxing,

and config-driven workflow orchestration engine design.

? AI System Verification & Data Engineering:

? Proven experience establishing evaluation metrics, testing benchmarks, and safety

guardrails for non-deterministic AI systems in high-stakes environments.

? Technical depth in Vector Databases, and cloud-native containerized orchestration

## Description

The Central Engineering Group is seeking an energetic, visionary, and passionate Agentic AI Architect to

spearhead the design, deployment, and optimization of cutting-edge autonomous multi-agent AI

architectures. In this strategic engineering role, you will be instrumental in revolutionizing IP block

development and support workflows across complex semiconductor design phases. You will leverage

state-of-the-art LLM orchestration, Model Context Protocol (MCP) integrations, and enterprise-grade

verification frameworks to drive unprecedented efficiency, quality, and automation in hardware, ? Autonomous Multi-Agent System Architecture & Scaling

? Conceptualize, architect, deploy, and scale robust, distributed multi-agent AI frameworks

tailored to transform intricate semiconductor design workflows.

? Orchestrate long-horizon, autonomous agent executions capable of managing multi-step

reasoning, task decomposition, and decision-making across all phases of chip

development and IP block lifecycle management.

? Enterprise Tool Ecosystem Integration & Protocols

? Seamlessly integrate Large Language Model (LLM)-driven agent pipelines with core

Electronic Design Automation (EDA) suites, custom in-house software tools, and

enterprise data repositories.

? Drive standardized tool-use integration protocols, including the Model Context Protocol

(MCP), ensuring seamless interoperability across heterogeneous software environments.

? Governance, Risk Mitigation & Safety Guardrails

? Formulate and implement comprehensive human-in-the-loop (HITL) review gates,

deterministic verification logic, and safety guardrails to maintain zero degradation in IP

quality

? Integrate end-to-end auditability, traceability, and operational safety for non-deterministic

AI agent outputs within mission-critical hardware design routines.

? Advanced Context & Memory Engineering

? Architect and optimize persistent short-term and long-term memory architectures that

empower autonomous agents to maintain contextual continuity.

? Enable agents to retain project state across iterative simulation regressions, complex

debugging sessions, and hardware bring-up feedback loops.

? Cross-Domain Strategic Orchestration

? Partner collaboratively with Design Leads, EDA Tool Specialists, and Infrastructure

Engineers to distill complex, domain-specific hardware engineering methodologies into

modular, goal-driven agentic workflows.

? Align AI workflow capabilities directly with organizational efficiency targets and technical