Senior Performance Engineer - AI-Driven Load & Reliability

AgileEngine

Seymour (CT)

On-site

USD 120,000 - 180,000

Full time

28 hours ago

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Benefits offered by this job

Growth without limits

Competitive compensation

Flexibility: 100% remote with flexible

Meaningful, modern projects

Collaborative culture

Well-being & support

Job summary

AgileEngine is seeking a Senior Performance Engineer to own a global, enterprise-wide Performance Engineering function across a digital ecosystem including web, mobile, kiosk, POS, loyalty and payments. You will design load, stress, and endurance tests with tools like JMeter or k6 and integrate automated validation into Azure DevOps CI/CD pipelines.

You will partner with SRE/DevOps on chaos testing, define reliability targets, and drive AI-assisted workflows for performance planning and triage,

Qualifications

4+ years of experience in performance engineering or non-functional testing at enterprise scale.
Hands-on experience with performance testing tools (e.g., JMeter, k6, LoadRunner, NeoLoad, or OctoPerf).
Experience with enterprise observability and cloud monitoring platforms (e.g., Dynatrace).
Demonstrated experience integrating automated performance validation into CI/CD pipelines (e.g., Azure DevOps).
Scripting and automation proficiency in Java, JavaScript, or Python.
Hands-on engineering experience in cloud environments (AWS or Azure).
Hands-on experience using AI-assisted engineering tools, including reviewing and validating AI-generated output.

Responsibilities

Own the enterprise Performance Engineering charter as a horizontal shared service and set SDLC performance-readiness standards.
Design, script, run, and report performance tests across high-volume ordering, kiosk, POS, loyalty, and payments flows.
Identify and resolve bottlenecks across app, database, messaging, and infrastructure layers using observability signals.
Embed automated performance validation into CI/CD pipelines with baseline and threshold gating.
Partner with SRE/DevOps to define reliability targets and conduct chaos-engineering experiments.
Define performance-readiness criteria and provide evidence-based release go/no-go recommendations.