AI Senior Engineer

INFOSYS NOVA HOLDINGS•Cupertino, CA

2d•Hybrid

About The Position

This role focuses on leveraging Generative AI (GenAI), Large Language Models (LLMs), and agents to enhance software quality engineering and test lifecycle automation. The successful candidate will be responsible for converting various documentation into executable test scenarios, building AI-assisted solutions for requirement analysis and defect management, and developing AI/LLM-based validation for multilingual content. A strong understanding of accessibility standards and experience with automation frameworks, CI/CD integration, and RAG pipelines are essential. The role also involves defining the AI-QE Solution architecture and leading technical discussions.

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 database 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.
Authorized to work for ANY employer in the U.S.

Nice To Haves

Experience with Kaleidoscope

Responsibilities

Convert requirements, user stories, specifications, API contracts, and technical documentation into executable test scenarios and test cases.
Build solutions that 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 automation frameworks, 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.