Lead Performance & AI Tools Engineer, Hybrid

Cisco•Milpitas, CA

3d•$186,900 - $267,700•Hybrid

About The Position

Cisco’s Developer Platforms and AI Productivity team builds internal tools, developer workflows, and autonomous AI agents for engineers across the company. The team develops platforms that accelerate software delivery, reduce development friction, and support reliability across Cisco’s cloud-native portfolio. As a Technical Leader in Developer Productivity & Performance Engineering, you will own the architecture of intelligent developer platforms and AI agent tooling. You will automate software delivery workflows such as pull request analysis, CI failure triage, test selection, and release readiness. You will also apply performance engineering to AI model endpoints and distributed Kubernetes workloads to optimize throughput and latency. The role aims to improve developer velocity and cycle times while enabling the safe adoption of AI workflows across Cisco engineering. This is a hybrid position based out of Cisco’s San Jose office. The application window is expected to close on December 20, 2026.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related STEM field and 10 or more years of relevant software engineering experience; or 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 within engineering delivery pipelines.
Experience conducting performance engineering, load testing, and workload modeling for distributed systems using Locust or equivalent frameworks.
Experience operating in Kubernetes and containerized cloud environments, using observability platforms such as Grafana, Datadog, or Splunk, and working with 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 using AI coding assistants and LLM-powered development tools to accelerate development, debugging, and test automation while ensuring correctness, maintainability, and security of generated code.
Ability to independently identify engineering workflow inefficiencies, deliver practical automation solutions, and evaluate effectiveness through developer feedback and measurable productivity improvements.

Nice To Haves

Experience architecting multi-agent orchestration, tool calling, Model Context Protocol (MCP), or vector-based engineering knowledge retrieval systems adopted across multiple teams.
Track record defining quantitative developer productivity metrics, such as PR cycle time, CI wait time, or time to first token, and driving broad internal tooling adoption.
Experience optimizing and benchmarking LLM inference endpoints, including TTFT, token throughput, and GPU utilization, across AWS, GCP, or Azure.
Familiarity with UI and browser performance automation frameworks such as Playwright and Lighthouse, and OpenTelemetry distributed tracing standards.
Strong cross-functional leadership, mentorship, and communication skills, including translating 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 outputs.
Experience integrating automated performance regression detection, baseline comparisons, and actionable performance feedback into pull requests and CI/CD workflows.

Responsibilities

Architect and deploy AI-assisted developer tools, including agents for pull request analysis, CI failure diagnosis, and engineering knowledge retrieval.
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.
Establish safe agent execution frameworks through sandboxing, scoped permissions, and confidence thresholds.
Unify observability and telemetry from Datadog, Grafana, Splunk, and OpenTelemetry to identify systemic bottlenecks and provide architectural recommendations.
Build and ship developer productivity platforms, rather than only designing AI agents.
Use AI-assisted development to accelerate engineering delivery.
Drive measurable developer productivity improvements and adoption across engineering teams.

Benefits

Medical, dental, and vision insurance
401(k) plan with Cisco matching contribution
Paid parental leave
Short- and long-term disability coverage
Basic life insurance
Potential eligibility for Cisco restricted stock unit grants
Paid holidays and a floating holiday for non-exempt employees
Paid birthday day off, year-end holiday shutdown, and personal wellness days
Paid vacation or flexible vacation time off, depending on employee classification
Paid sick time
Additional paid time away may be requested for critical or emergency family issues
Optional paid volunteer days
Annual bonuses for eligible non-sales roles