C

Cisco

Makes networking gear and security software

Lead Performance & AI Tools Engineer

AI Application EngineeringPosted on Nov 5
Full-timeSan Jose, CA, USA

About the Job

Requirements

  • A Bachelor’s degree in Computer Science, Engineering, or a related STEM field and 10 or more years of relevant software engineering experience; a Master’s degree and 7 or more years; or a Ph.D. and 4 or more years building distributed platforms.
  • At least 5 years of software development experience using Python to design, build, and deploy production internal engineering platforms, automation frameworks, or developer productivity tools.
  • Experience implementing LLM APIs, AI coding assistants, or autonomous agent workflows, such as retrieval-augmented generation, pull request analysis, or automated failure diagnosis, within engineering delivery pipelines.
  • Experience conducting performance engineering, load testing, and workload modeling for distributed systems using Locust or equivalent load-testing frameworks to analyze latency, throughput, and resource utilization.
  • Experience operating within Kubernetes and containerized cloud environments, using observability platforms such as Grafana, Datadog, or Splunk, and distributed tracing.
  • Experience integrating developer tools and automation workflows with Git-based source control, pull requests, CI/CD pipelines, APIs, and automated testing frameworks.
  • Experience leveraging AI coding assistants and LLM-powered development tools to accelerate software development, debugging, and test automation while ensuring generated code correctness, maintainability, and security.
  • Ability to independently identify engineering workflow inefficiencies, design and deliver practical automation solutions, and evaluate their effectiveness through developer feedback and measurable productivity improvements.

Responsibilities

  • Architect and deploy AI-assisted developer tools, including intelligent agents for automated pull request analysis, CI failure diagnosis, and engineering knowledge retrieval, to reduce developer cycle time and manual debugging.
  • Design and implement load, stress, and scalability testing using Python and Locust to optimize latency, throughput, and token generation rates across distributed services and model endpoints.
  • Implement sandboxing, scoped permissions, and confidence thresholds so AI developer tools operate reliably, securely, and transparently.
  • Correlate metrics from Datadog, Grafana, Splunk, and OpenTelemetry to identify systemic bottlenecks and provide actionable architectural recommendations to engineering leadership.
  • Build and ship developer productivity platforms, not just design AI agents.
  • Use AI-assisted development to accelerate engineering delivery, rather than only integrating LLMs into applications.
  • Drive measurable productivity improvements and adoption across engineering teams.

Desired Qualifications

  • Experience architecting multi-agent orchestration, tool calling, Model Context Protocol (MCP), or vector-based engineering knowledge retrieval systems adopted across multiple teams.
  • A proven track record defining quantitative developer productivity metrics, such as pull request cycle time, CI wait time, and time to first token, and driving widespread internal tooling adoption.
  • Experience optimizing and benchmarking LLM inference endpoints, measuring time to first token, token throughput, and GPU utilization, across AWS, GCP, or Azure.
  • Familiarity with UI/browser performance automation frameworks such as Playwright and Lighthouse, and OpenTelemetry distributed tracing standards.
  • Strong cross-functional leadership, mentorship, and communication skills, with the ability to translate complex telemetry data into actionable engineering roadmaps.
  • Experience building AI-powered developer productivity solutions for code-change impact analysis, dependency-aware test selection, automated CI failure triage, or intelligent release validation.
  • Experience designing and evaluating AI-agent workflows for accuracy, reliability, cost, latency, and safe execution, including tool-use guardrails, human approval mechanisms, and handling incorrect or uncertain AI outputs.
  • Experience integrating automated performance regression detection, baseline comparisons, and actionable performance feedback into pull requests and CI/CD workflows.

About the Company

Cisco

Makes networking gear and security software

Cisco makes the switches and routers that connect offices, data centers, and cloud services, and it also sells cybersecurity tools and Webex video meetings to businesses and governments. It was one of the first companies to sell routers that let different kinds of computer networks exchange data, and in 2000 it briefly became the world's most valuable company. Customers run its network gear, from Wi-Fi access points to AI data center systems, with Cisco software that sets up and secures the network, while its Splunk unit searches computer logs to catch outages and attacks. Cisco earns money from hardware sales, software subscriptions, and support contracts. It was founded in 1984 and is based in San Jose, California.

Company size
10,001+
Company stage
IPO
Headquarters
San Jose, CA
Founded
1984

Recently Posted Jobs

Explore more opportunities at Cisco

See more jobs