Quality Engineer AI Cloud- LMTS

Job Description

About the team:

Join the AI Cloud Quality Engineering team, and become a specialist on Salesforces AI Platform and applications You will be a key member of the Quality Engineering team in India as we build the India organization, while working with experts worldwide. Youll get to work with latest technology in the AI space (including generative AI), and collaborate with the team and cloud to identify and run quality initiatives to support massive scale planned both in the short term and long term. We are a small, friendly team that has been working together for years - outside of quality, we focus on volunteering and other shared interests.

Job description 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.

Skills needed for role:

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 application s 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.

Nice to have skillsets (or skillsets expect to develop in this role):

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

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