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Tier-1 brand, metro location, and broad skillset increase applicant density, balanced by senior niche LLMOps needs.
Combination of QA automation and LLMOps skills is moderately transferable across industries.
Mandatory 8+ years plus specific QA, automation and LLMOps tech requirements raise filter strictness.
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Lead design and implementation of ML pipelines, including experiment, model, and feature management, with scalable API inference.
Drive QA automation initiatives to achieve full automation coverage, execution, and high code coverage, including AI-driven self-healing and intelligent test execution.
Plan and manage automated testing activities, covering frontend, backend, data pipelines, performance, and scalability in Agile environments.
5-8 years of QA Automation experience in Java/J2EE and web-based applications.
Proficient in automation tools like Selenium, TestNG, JUnit; programming in Java or equivalent OOP languages; exposure to Python and AI/ML frameworks such as Huggingface, PyTorch, TensorFlow.
Bachelor’s or Master’s degree in Computer Science, Engineering, MBA, or equivalent.
Experience with containerization and orchestration tools (Docker, Kubernetes) and knowledge of cloud ML platforms (MLflow, SageMaker, Vertex AI, Azure AI).
Experienced in leading QA automation for data analytics and AI/ML projects with strong ownership of automation KPIs and framework development.
Skilled in AI/ML model lifecycle management, large language model training/serving, and DevOps practices relevant to AI (LLMOps).
Comfortable working in fast-evolving environments with limited standardization, collaborating across teams, and incorporating AI agents in automation processes.