2 Lmts Jobs

Primary Responsibilities: Own end-to-end quality and performance test scenarios needed to cover Mulesoft Services and Products. Execute performance test scenarios, analyze data, automate and identify bottlenecks (manually or via ML) and work with the Engineering teams to address issues. Solve difficult performance issues: Work with service owners closely to improve baseline performance, reduce resource consumption and shorten request latency. Provide input and guidance to our SRE and Platform teams to determine sizing, scaling and utilization parameters for various service tiers, architecting for resiliency and low MTTR. Assist in driving further adoption of reliability practices such as Chaos engineering, GameDays, Performance SLO to achieve site readiness Leverage Kubernetes internals like Operator frameworks to expand our scope and coverage of testing, composing environments as necessary to independently test. Shift performance testing leftward across Mulesoft, enabling early stage continuous performance testing at each service level. Work with counterparts in the Functional Test and Engineering Services organizations to build tooling that addresses common needs. Enable self-serve for customers, the field, pre-sales and support through maintenance and update of performance related documentation and tuning guides. Qualifications: 2-10+ years of solid cloud platform experience as a quality or performance engineer working in a distributed or enterprise environment with a good track record of technical leadership. BS/MS in CS/EE or equivalent. Passionate with a deep technical skill in figuring out what makes things work, with a quality mindset and tuning of complex distributed computing systems, especially in cloud based environments. Unparalleled troubleshooting and problem-solving skills. Superb written and verbal communication skills, as exemplified by clear bug/issue explanations, documentation of knowledge, mentoring ability, and comfort in interacting with all levels of management.

Job summary: As a Quality Engineer for the Salesforce AI Cloud, you will be responsible for leading the design, development and execution of comprehensive test plans/quality strategies with emphasis on automation . You will collaborate closely with product & engineering teams, actively contributing to the quality of new features while ensuring successful execution of regression tests in various environments. You will be working in a fast phased environment and will be expected to understand complex backend AI flow systems/dependencies, identifying potential risks, and strategically plan and execute comprehensive testing efforts to ensure the stability, reliability and quality of the feature and release. Required Skills Strategic and Tactical leadership : Experience leading analysis of coverage gaps and writing E2E and integration tests, partnering with teams to drive service level test coverage Technical Expertise: Technical excellence and leadership required in identifying, developing and maintaining tests involving API, databases and web UI tests. Strong knowledge of automation tools and continuous automation systems. Cross Team Collaboration: Ability to work with stakeholders (internal customers) from multiple organizations that leverage our central platform for custom applications. Collaborate with them to determine key shared usage patterns to prioritize test coverage of, as well as provide guidance to these customers on how to proactively test their applications integration with the platform. Strong knowledge and experience of Java/Python/Selenium/CD pipeline configurations Excellent communication skills: Experience generating reports, presenting and holding the quality bar for new features & release Experience working in Global teams: Ability to work and partner with peers and other partner teams across timezones. 8+ years of QE experience and 2+ years of experience leading projects and partnering/leading junior members of the team. Deisred Skills Understanding of LLM Model hosting with Sagemaker Understanding LLM Finetuning and Inferencing Testing integrations with Cohere, Anthropic, Dolly, Google, etc. as well as internal models Sagemaker GroundTruth integration for data labelling, Bedrock for model serving Experience with Azure Open AI Experience with Tableau around creating dashboards for reporting What the team does: Center of Excellence for Quality within the AI Platform teams, driving excellence through automation, tools, and streamlined processes (evangelizing shift left methodologies) . Building/enhancing test frameworks/tooling for the easy creation and maintenance of integration and end-to-end tests. Proactively drive quality by monitoring and reporting of test stability metrics for pre-prod & Prod environments through dashboards, triaging and filing and follow up of bugs. CUJs(Critical User Journey test) for cross-cloud integration and apps Configure tooling/runners for automated tests, used by scrum teams Configure various test environments such as setting up core test envhawking test env connectivity Define and measures Quality metrics (code coverage, endpoint coverage, service coverage, etc.) goals are met for the ML foundations Automation strategies and framework support for all kinds of testing Identify risks with releases, deployments, etc. Support TAM expansion to multiple geographies and monitoring platform stability Daily management of Integration tests pass rates in Test, App-Dev, Stage & Prod environments - bugs created/assigned if failures not being addressed, building test result dashboards and simplifying feedback/debugging process for engineers