Architect, AI / Agentic Automation

Cisco

Confirmed live today High trust
Closes in 5 days Hybrid

Quick summary

Work type
Hybrid
Location
San Jose, CA
Employment
Full-time
Posted
61 days ago
Freshness
Confirmed live today
Closes
Nov 1, 2026 (soon)

Market check

Salary context

How this pay compares to similar roles

Similar $205k
$145k $269k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $163,020–$246,131.

Based on 240 similar postings.

Employer

About Cisco

Cisco Systems is the world''s leading networking technology company, designing and manufacturing networking hardware, telecommunications equipment, and cybersecurity solutions for businesses and governments. Industry: Networking Technology & Cybersecurity

Cisco currently has 307 open roles on FindRole.

Listed pay typically runs $167,700–$245,000 across 224 roles with salary data.

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At a glance

TL;DR · Architect, AI / Agentic Automation

Architect – AI / Agentic Automation The Architect – AI / Agentic Automation joins the Cisco IQ and Validation team to build next-generation intelligent, AI-driven network validation and automation capabilities. This role involves defining the architecture for AI decision systems, Agentic AI platforms, and enterprise-scale validation workflows. You will establish technical direction for agent architectures, MCP and A2A integrations, tool orchestration, and distributed event-driven systems. Key responsibilities include guiding architecture across backend services, APIs, databases, Kubernetes, and cloud infrastructure while mentoring senior engineers. Required technical skills include Python, Go, Rust, TypeScript, React, Node.js, .NET, PostgreSQL, Redis, Kafka, and Docker. You will solve complex problems regarding multi-agent architectures, including planner/executor patterns, state management, and durable workflows, to enable AI agents to reason and interact with enterprise tools safely.

What does a Architect earn?

Median $225000 from 45 postings across 26 companies.

See salary data

What you'll do

  • Define and drive the architecture for AI decision systems and Agentic AI platforms.
  • Establish technical direction for agent architectures, MCP and A2A integrations, and tool orchestration.
  • Guide architecture for backend services, APIs, databases, Kubernetes, and cloud infrastructure.
  • Ensure solutions are scalable, secure, reliable, and maintainable across production operations.
  • Provide technical leadership for complex, ambiguous, and cross-functional initiatives.
  • Mentor senior engineers and technical leads on engineering standards and platform strategy.
  • Design advanced multi-agent architectures including planner/executor patterns and durable workflows.

What we're looking for

  • Bachelor’s degree in STEM.
  • 8+ years of software engineering experience designing and building complex backend, distributed, automation, or platform systems.
  • Experience with Python, Go, Rust, TypeScript, React, Node.js, .NET, PostgreSQL, Redis, Kafka, or similar technologies.
  • Experience designing distributed systems using APIs, databases, event-driven architectures, workflow systems, or cloud-native technologies.
  • Experience developing or architecting Generative AI, LLM, Agentic AI, MCP, A2A, or AI-enabled automation systems.
  • Experience with Kubernetes, Docker, CI/CD, cloud platforms, and production software operations.
  • Experience designing advanced multi-agent architectures, including planner/executor patterns, agent handoffs, state management, checkpointing, retries, cancellation, or durable workflows.
  • Experience designing AI quality, evaluation, observability, and governance frameworks (preferred).
  • Experience with network automation, device connectivity, validation platforms, infrastructure automation, or enterprise network systems (preferred).
  • Experience leading architecture across multiple engineering teams and influencing technical strategy, standards, and platform roadmaps (preferred).
  • Experience architecting MCP and A2A systems, including capability discovery, tool schemas, authentication, context propagation, streaming, approval gates, auditability, and failure handling (preferred).

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