Test Engineer LMTS/PMTS | Salesforce | Bengaluru, Karnataka, India

Position: Test Engineer LMTS/PMTS

Location: Bengaluru, Karnataka, India

Job Description

Qualification and Skills

Test Gap Analysis & Coverage: Experience analyzing coverage gaps and writing E2E and integration tests, partnering with teams to drive service level test coverage
Technical Expertise: Technical excellence and leadership required to learn a new platform and drive end to end test strategy for it, partnering with Quality Engineering team, Hawking scrum teams and customer cloud Q3s to implement.
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.
Experience with Java/Python
Experience with test runners and configuration
Extensive experience debugging systems
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 labeling, Bedrock for model serving
Experience with Azure Open AI

Daily management of FIT 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
Integration testing for hawking, generative AI flows
Building/enhancing test frameworks for Hawking platform
CUJs (Critical User Journey test) for cross-cloud integration and apps
Configure tooling/runners for FIT tests, used by scrum teams
Configure various test environments such as setting up core test env ↔ hawking test env connectivity
Manage test coverage (code coverage, endpoint coverage, service coverage, etc.) goals are met for the ML foundations
Automation strategies and framework for certain end-to-end scenarios
Identify risks with releases, deployments, etc.

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