Agentic AI Architect

Location: Saint Paul

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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 frameworkstailored 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 chipdevelopment 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 IPquality Integrate end-to-end auditability, traceability, and operational safety for non-deterministicAI 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, complexdebugging 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 intomodular, goal-driven agentic workflows. Align AI workflow capabilities directly with organizational efficiency targets and technicalmilestones 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 knowledgesources – 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 Lang Graph. Semiconductor Domain Knowledge & EDA Tooling:
Fundamental grasp of chip design methodologies, IP block development life cycles, and verification environments. Hands-on familiarity with industry-standard EDA tooling and frameworksfrom 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 safetyguardrails for non-deterministic AI systems in high-stakes environments. Technical depth in Vector Databases, and cloud-native containerized…