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Job 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
engineering environments.
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.
Key Job Responsibilities
● 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
milestones across the Central Engineering Group.
Required Skillset & Technical Competencies
● AI Engineering & Multi-Agent Expertise:
● 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…