AI Senior Engineer

INFOSYS NOVA HOLDINGS LLC•Cupertino, CA

5h•$110,000 - $120,000•Hybrid

About The Position

This role focuses on leveraging Generative AI (GenAI), Large Language Models (LLMs), and AI agents to enhance software quality engineering and automate the test lifecycle. The engineer will be responsible for converting requirements into executable test scenarios, designing AI-assisted workflows for various quality assurance tasks, and developing AI/LLM-based validation mechanisms. A key aspect of the role involves defining the AI-QE Solution architecture and technical roadmap, while also leading technical discussions and translating business problems into scalable AI solutions.

Requirements

Strong practical experience with GenAI, LLMs, and agents AI to software quality engineering and test lifecycle automation.
Ability to convert requirements, user stories, specifications, API contracts, and technical documentation into executable test scenarios and test cases.
Experience building solutions that analyze requirements for functional gaps, ambiguity, traceability, risk, and test coverage.
Hands-on experience designing multi-agent workflows for requirement analysis, test generation, defect analysis, validation, and quality intelligence.
Capability to develop AI-assisted mechanisms for defect identification, classification, deduplication, severity assessment, root-cause analysis, and automated defect filing.
Experience in developing AI/LLM-based validation for multilingual and localized content, including translation accuracy, formatting, connect, truncation, and content consistency.
Strong understanding of accessibility standards such as WCAG 2.2 with the ability to build AI-assisted automated checks for accessibility violations across web experiences.
Strong experience with Automation frameworks API/UI testing, CI/CD integration, test orchestration, reporting.
Experience building RAG pipelines using embeddings and vector databases to ground AI generated test scenarios and validations in approved requirements and product knowledge.
Ability to define the AI-QE Solution architecture, technical roadmap, reusable accelerators, engineering standard, and measurable business outcomes.
Strong hands-on expertise in Python and Java/Typescript with experience integrating LLM and AI services through APIs.
Ability to lead technical discussions with QE, engineering, product, and client stakeholders and translate business problems into scalable AI solutions.
Must be authorized to work for ANY employer in the U.S.

Responsibilities

Apply GenAI, LLMs, and AI agents to software quality engineering and test lifecycle automation.
Convert requirements, user stories, specifications, API contracts, and technical documentation into executable test scenarios and test cases.
Build solutions to analyze requirements for functional gaps, ambiguity, traceability, risk, and test coverage.
Design multi-agent workflows for requirement analysis, test generation, defect analysis, validation, and quality intelligence.
Develop AI-assisted mechanisms for defect identification, classification, deduplication, severity assessment, root-cause analysis, and automated defect filing.
Develop AI/LLM-based validation for multilingual and localized content, including translation accuracy, formatting, connect, truncation, and content consistency.
Build AI-assisted automated checks for accessibility violations across web experiences, adhering to WCAG 2.2 standards.
Integrate AI solutions with Automation frameworks for API/UI testing, CI/CD, test orchestration, and reporting.
Build RAG pipelines using embeddings and vector databases to ground AI-generated test scenarios and validations in approved requirements and product knowledge.
Define the AI-QE Solution architecture, technical roadmap, reusable accelerators, engineering standards, and measurable business outcomes.
Lead technical discussions with QE, engineering, product, and client stakeholders.
Translate business problems into scalable AI solutions.