Lead Performance & AI Tools Engineer, Hybrid

Cisco•Milpitas, MI

2d•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 aims to accelerate software delivery, reduce development friction, and support high reliability across Cisco’s global cloud-native portfolio. This role offers the opportunity to advance AI-assisted software engineering by integrating coding agents, automated triage, and performance frameworks at enterprise scale. As a Technical Leader in Developer Productivity and Performance Engineering, the successful candidate will own the architecture of intelligent developer platforms and AI agent tooling. The role turns software delivery bottlenecks into automation across pull request analysis, CI failure triage, test selection, and release readiness. It also applies performance engineering to AI model endpoints and distributed Kubernetes workloads to improve throughput and latency. Success is measured through improvements in developer velocity and cycle time, as well as adoption of secure, sandboxed AI workflows across Cisco engineering.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related STEM field and 10+ years of relevant software engineering experience; or a master’s degree and 7+ years; or a Ph.D. and 4+ years building distributed platforms.
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 load-testing frameworks.
Experience operating 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 software development, debugging, and test automation while ensuring generated code is correct, maintainable, and secure.
Ability to independently identify engineering workflow inefficiencies, design and deliver automation solutions, and evaluate outcomes 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.
A track record of 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 measuring TTFT, 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.
Cross-functional leadership, mentorship, and communication skills, including the ability to translate 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, build, and deploy AI-assisted developer tools, including agents for automated 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 using sandboxing, scoped permissions, and confidence thresholds.
Correlate telemetry from Datadog, Grafana, Splunk, and OpenTelemetry to identify systemic bottlenecks and provide architectural recommendations to engineering leadership.
Build and ship developer productivity platforms, rather than only designing AI agents.
Use AI-assisted development to accelerate engineering delivery.
Drive measurable productivity improvements and adoption across engineering teams.
Identify workflow inefficiencies, deliver practical automation, and evaluate its effectiveness through developer feedback and productivity measures.

Benefits

Medical, dental, and vision insurance, subject to plan eligibility rules.
401(k) plan with a Cisco matching contribution.
Paid parental leave.
Short- and long-term disability coverage.
Basic life insurance.
Potential eligibility for Cisco restricted stock unit grants, subject to continued employment and vesting terms.
Paid holidays and a floating holiday for non-exempt employees.
A paid day off for employees’ birthdays, a paid year-end holiday shutdown, and paid personal wellness days.
Paid vacation time for non-exempt employees; exempt employees participate in a flexible vacation time off program, subject to availability and business limitations.
Paid sick time and carryover of unused sick time, subject to Cisco policy.
Additional paid time away may be requested for critical or emergency family issues.
Optional paid volunteer days.
Annual bonuses for non-sales roles, subject to Cisco policies.
Sales incentive compensation for employees on sales plans, subject to the applicable plan.