Senior performance Tester with AI - India - People Prime Worldwide

    People Prime Worldwide
    People Prime Worldwide India

    1 day ago

    Full time
    Description

    About Company

    Our client is a trusted global innovator of IT and business services. We help clients transform through consulting, industry solutions, business process services, digital & IT modernisation and managed services. Our client enables them, as well as society, to move confidently into the digital future. We are committed to our clients' long-term success and combine global reach with local client attention to serve them in over 50 countries around the globe.

    Job Title: Senior performance Tester with AI

    Location: Pan India/Remote

    Experience: 8+ yrs

    Job Type: Contract to hire

    Notice Period:- Immediate joiner

    Mandatory Skills:

    Automation & Engineering:


    • Build performance test frameworks as code using NeoLoad, JMeter, and Gatling; create reusable libraries, custom plugins, and data/model generators.


    • Implement CI/CD pipelines (Jenkins, Bitbucket, Git/GitHub, GitHub Actions) with YAML-based pipeline templates and standardised stages (provision → run → analyse → gate → publish).


    • Integrate tests with Dynatrace (APM, distributed tracing), OpenTelemetry, and Prometheus/Grafana; automate metric ingestion, correlation IDs, and SLO-based release gates.


    • Establish shift-left-left performance (component/API/microservice) and shift-right-right performance (canary, blue/green, traffic replay), with automated regression detection and baseline drift alerts.


    • Automate reporting & insights: generate dashboards, trend analyses, comparative baselines (on-prem vs cloud), and actionable remediation plans.

    Coverage & Scenarios


    • REST, GraphQL (query cost/complexity, N+1 detection, resolver latency), gRPC, HTTP/2, WebSockets.


    • EDI/Edifecs flows (AS2/SFTP/HTTPS), X12/HL7 parsing/validation, file transfer throughput, file processing end-to-end-to-end times, acknowledgements (TA1/999


    • Microservices workloads (sync/async), event streams (Kafka), DB/cache tiers, object/block storage, and network egress/ingress.


    • Resilience & scalability testing: autoscale warm-up, cold starts, failover, chaos (Azure Chaos Studio/Gremlin), backpressure, DLQ, idempotency.


    • Automate root cause analysis with APM + traces + logs; build correlation between test steps and service spans.

    Required Skills & Experience:


    • Load Testing (expert): NeoLoad, JMeter, Gatling; distributed load generation; custom plugins;


    • Languages: Java & Python (test utilities, analysis, CLI tools, API clients).


    • Pipelines as Code: Strong YAML expertise; Jenkins, GitHub Actions, Bitbucket; reusable pipeline templates and governance.


    • Cloud & Containers: Microsoft Azure, AKS (Pods, Deployments), Helm, Argo CD; Terraform/ARM/Bicep.


    • Observability & APM: Dynatrace (service maps, Davis AI, dashboards), OpenTelemetry, Prometheus/Grafana; metric & trace correlation.


    • APIs: REST & GraphQL performance testing (added advantage), gRPC/HTTP2; authentication (mTLS/OAuth2) performance impacts.


    • Data & Messaging: Kafka, SQS/Event Hub equivalents, SQL/NoSQL performance patterns, and storage performance (IOPS/throughput).


    • Project Leadership: Led at least one end-to-end cloud performance programme with full CI/CD automation and release gates.

    Impact & Deliverables (Automation First)


    • Performance Test Frameworks as Code with libraries, examples, and documentation.


    • Self-service CI/CD pipelines (one-click or PR-triggered) that provision env, execute tests, analyse results, and publish gates.


    • Production-like telemetry dashboards (Dynatrace) with automated reports: p50/p90/p95/p99 latency, throughput, error rates, resource efficiency, and cost/TPS.


    • Automated baselines & drift detection, canary comparisons, and regression alerts.


    • Runbooks & playbooks for scaling, tuning, and incident response; performance sign-off criteria aligned to SLAs.


    • Teams can self-serve performance runs and consume actionable insights with minimal engineering assistance.

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